Chapter 3 -- vocabulary
preferential trade agreement -- a mechanism that confers special treatment on select trading partners
free trade area (FTA) -- formed when two or more countries agree to eliminate tariffs and other barriers that restrict trade
free trade agreement -- the ultimate goal of which is the rare duties on goods that cross borders between the partners
rules of origin -- used to discourage the importation of goods into the member country
customs union -- represents the logical evolution of a free trade area
common external tariffs (CETs) -- When a goup of countries form a customs union they must introduce a common external tariff. The same customs duties, quotas, preferences or other non-tariff barriers to trade apply to all goods entering the area, regardless of which country within the area they are entering. It is designed to end re-exportation
common market -- Where two or more countries agree to form a customs union between themselves and a common external tariff against goods and commodities imported from other countries
economic union -- builds upon the elimination of the internal tariff barriers, the establishment of common external barriers, and the free flow of factors. It seeks to coordinate harmonize economic and social policy within the union to facilitate the free flow of capital, labor, and goods and services from country to country
Saturday, June 23, 2007
in the global trade environment
Chapter 3 -- summary
The multilateral World Trade Organization, created in 1995 as the successor to the General Agreement on Tariffs and Trade, provides a forum for settling disputes among member nations and tries to set policy for world trade. The world trading environment is also characterized by preferential trade agreements among smaller numbers of countries on the regional and some regional basis. These agreements can be conceptualized on a continuum of increasing economic integration. Free trade areas such as the one created by the North American Free Trade Agreement (NAFTA) represent the lowest level of economic integration. The purpose of a free trade area is to eliminate tariffs and quotas.
Rules of origin are used to verify the country from which the goods are shipped. A customs union represents a further degree of integration in the form of common external tariffs. And a common market such as the Central American Integration System (SICA), restrictions on the movement of labor and capital are based in an effort to further increase integration. In an economic union, such as the European Union (EU), the highest level of economic integration is achieved by unification of economic policies and institutions. Other important call Thracian agreements include the Association of Southeast Asian Nations (ASEAN) and Quad ration Council for the Arab States of the Golf (GCC). In Africa, the two main cooperation agreements are the Economic Community of West African States (ECOWAS) and the South African Development and community (SADC).
The multilateral World Trade Organization, created in 1995 as the successor to the General Agreement on Tariffs and Trade, provides a forum for settling disputes among member nations and tries to set policy for world trade. The world trading environment is also characterized by preferential trade agreements among smaller numbers of countries on the regional and some regional basis. These agreements can be conceptualized on a continuum of increasing economic integration. Free trade areas such as the one created by the North American Free Trade Agreement (NAFTA) represent the lowest level of economic integration. The purpose of a free trade area is to eliminate tariffs and quotas.
Rules of origin are used to verify the country from which the goods are shipped. A customs union represents a further degree of integration in the form of common external tariffs. And a common market such as the Central American Integration System (SICA), restrictions on the movement of labor and capital are based in an effort to further increase integration. In an economic union, such as the European Union (EU), the highest level of economic integration is achieved by unification of economic policies and institutions. Other important call Thracian agreements include the Association of Southeast Asian Nations (ASEAN) and Quad ration Council for the Arab States of the Golf (GCC). In Africa, the two main cooperation agreements are the Economic Community of West African States (ECOWAS) and the South African Development and community (SADC).
Friday, June 22, 2007
the global economic environment
Chapter 2 -- vocabulary
market capitalism -- an economic system in which individuals and firms allocate resources and production resources are privately owned. Consumers decide what goods they desire and firms determined what and how much to produce
Centrally planned socialism -- the state has broad powers to serve the public interest as it sees fit. State planners may decisions about what goods and services are produced and in what quantities; consumers spend their money on what is available. Government ownership of entire industries, as well as individual enterprises
centrally planned capitalism and economic system in which command resource allocation is utilized extensively in an environment of private resource ownership
market socialism -- Mark allocation policies are permitted within an overall environment of state ownership
G-7 -- Group of 7 -- high income countries, the United States, Japan, Germany, France, Britain, Canada, and Italy. Finance ministers, central bankers, and heads of state from the seven nations have worked together for a quarter of a century in an effort to steer the global economy in the direction of prosperity and to ensure monetary stability
Organization for Economic Cooperation and Development (OECD) -- institution comprised of high income countries, the 30 nations that belong to the OECD believe in market allocation economic systems and pluralistic democracy
The Triad -- Japan, Western Europe, and the United States. These three regions, represent the dominant economic centers of the world. Today, nearly 75% of the world income is located in the Triad
purchasing power parity (PPP) --Model of exchange rate determination stating that the price of a good in one country should equal the price of the same good in another country, exchanged at the current rate
economic exposure -- impact of currency fluctuations on the present by you of the companies expected future cash flows
transaction exposure -- arises when the companies activities result in sales or purchases denominated in foreign currencies
hedging --Reducing exposure to risk of loss resulting from fluctuations in exchange rates, commodity prices, interest rates etc
forward market -- a mechanism for buying and selling currencies and a preset price for future delivery
market capitalism -- an economic system in which individuals and firms allocate resources and production resources are privately owned. Consumers decide what goods they desire and firms determined what and how much to produce
Centrally planned socialism -- the state has broad powers to serve the public interest as it sees fit. State planners may decisions about what goods and services are produced and in what quantities; consumers spend their money on what is available. Government ownership of entire industries, as well as individual enterprises
centrally planned capitalism and economic system in which command resource allocation is utilized extensively in an environment of private resource ownership
market socialism -- Mark allocation policies are permitted within an overall environment of state ownership
G-7 -- Group of 7 -- high income countries, the United States, Japan, Germany, France, Britain, Canada, and Italy. Finance ministers, central bankers, and heads of state from the seven nations have worked together for a quarter of a century in an effort to steer the global economy in the direction of prosperity and to ensure monetary stability
Organization for Economic Cooperation and Development (OECD) -- institution comprised of high income countries, the 30 nations that belong to the OECD believe in market allocation economic systems and pluralistic democracy
The Triad -- Japan, Western Europe, and the United States. These three regions, represent the dominant economic centers of the world. Today, nearly 75% of the world income is located in the Triad
purchasing power parity (PPP) --Model of exchange rate determination stating that the price of a good in one country should equal the price of the same good in another country, exchanged at the current rate
economic exposure -- impact of currency fluctuations on the present by you of the companies expected future cash flows
transaction exposure -- arises when the companies activities result in sales or purchases denominated in foreign currencies
hedging --Reducing exposure to risk of loss resulting from fluctuations in exchange rates, commodity prices, interest rates etc
forward market -- a mechanism for buying and selling currencies and a preset price for future delivery
the global economic environment
Chapter 2 -- summary
The economic environment is a major determinant of global market potential and opportunity. In today's global economy, capital movements are the key driving force, production has become uncoupled from employment, and capitalism has vanquished communism. Based on patterns of resource allocation and ownership, the world's national economies can be categorized as market capitalism, centrally planned capitalism, centrally planned socialism, and market socialism. The final years of the 20th century were marked by a transition toward market capitalism in many countries that had been centrally controlled. However, there still exists a great disparity among the nations of the world in terms of economic freedom.
Countries can be categorized in terms of their stage of economic development: low income, lower -- middle income, upper -- middle income, and high income. Countries in the first two categories are sometimes known as less developed countries (LDCs). Upper middle income countries with high growth rates are often called newly industrializing economies (NIEs). Several of the world's economies are notable for their fast growth; the big emerging markets (BEMs) include China and India (low income), Poland, Turkey, and Indonesia (lower middle income), Argentina, Brazil, Mexico, and South Africa (upper middle income), and South Korea (high income). The group of seven (G7) and Organization for Economic Cooperation and Development (OECD) represent two initiatives by high income nations to promote democratic ideals and free-market policies throughout the rest of the world. Most of the world's income is located in the Triad, which is comprised of Japan, the United States, and Western Europe. Companies with global aspirations generally have operations in all three areas. Market potential for a product can be evaluated by determining product saturation levels in light of income levels.
A countries balance of payments is a record of its economic transactions with the rest of the world; this record shows whether a country has a trade surplus (value of exports exceeded by you of imports) or a trade deficit (by you of imports exceeds value of exports). Trade figures can be further divided into merchandise trade and services trade accounts; a country can run a surplus in both accounts, a deficit in both accounts, or a combination of the two. The US merchandise trade deficit was 549 billion in 2003. However, the US enjoys an annual service trade surplus. Overall, the United States is a debtor; Japan enjoys an overall trade surplus and serves as a creditor nation.
Foreign exchange provides a means for settling accounts in different currencies. The dynamics of international finance can have a significant impact on the nation's economy as well as the fortunes of individual companies. Currencies can be subject to evaluation as a result of actions taken by a countries central banker. Currency trading by international speculators can also lead to evaluation.
When a country's economy is strong or when demand for its goods is high, its currency tends to appreciate in value. When currency bodies fluctuate, firms face various types of economic exposure. These include transaction exposure and operating exposure. Firms can manage exchange-rate exposure by hedging, for example, by buying and selling currencies and the forward market.
The economic environment is a major determinant of global market potential and opportunity. In today's global economy, capital movements are the key driving force, production has become uncoupled from employment, and capitalism has vanquished communism. Based on patterns of resource allocation and ownership, the world's national economies can be categorized as market capitalism, centrally planned capitalism, centrally planned socialism, and market socialism. The final years of the 20th century were marked by a transition toward market capitalism in many countries that had been centrally controlled. However, there still exists a great disparity among the nations of the world in terms of economic freedom.
Countries can be categorized in terms of their stage of economic development: low income, lower -- middle income, upper -- middle income, and high income. Countries in the first two categories are sometimes known as less developed countries (LDCs). Upper middle income countries with high growth rates are often called newly industrializing economies (NIEs). Several of the world's economies are notable for their fast growth; the big emerging markets (BEMs) include China and India (low income), Poland, Turkey, and Indonesia (lower middle income), Argentina, Brazil, Mexico, and South Africa (upper middle income), and South Korea (high income). The group of seven (G7) and Organization for Economic Cooperation and Development (OECD) represent two initiatives by high income nations to promote democratic ideals and free-market policies throughout the rest of the world. Most of the world's income is located in the Triad, which is comprised of Japan, the United States, and Western Europe. Companies with global aspirations generally have operations in all three areas. Market potential for a product can be evaluated by determining product saturation levels in light of income levels.
A countries balance of payments is a record of its economic transactions with the rest of the world; this record shows whether a country has a trade surplus (value of exports exceeded by you of imports) or a trade deficit (by you of imports exceeds value of exports). Trade figures can be further divided into merchandise trade and services trade accounts; a country can run a surplus in both accounts, a deficit in both accounts, or a combination of the two. The US merchandise trade deficit was 549 billion in 2003. However, the US enjoys an annual service trade surplus. Overall, the United States is a debtor; Japan enjoys an overall trade surplus and serves as a creditor nation.
Foreign exchange provides a means for settling accounts in different currencies. The dynamics of international finance can have a significant impact on the nation's economy as well as the fortunes of individual companies. Currencies can be subject to evaluation as a result of actions taken by a countries central banker. Currency trading by international speculators can also lead to evaluation.
When a country's economy is strong or when demand for its goods is high, its currency tends to appreciate in value. When currency bodies fluctuate, firms face various types of economic exposure. These include transaction exposure and operating exposure. Firms can manage exchange-rate exposure by hedging, for example, by buying and selling currencies and the forward market.
introduction to global marketing
Chapter 1 -- vocabulary
Global marketing -- involves an understanding of specific concepts, considerations, and strategies that must be skillfully applied in conjunction with Universal marketing fundamentals to ensure success in global markets
value chain -- The set of activities required to design, procure, produce, market, distribute, and service a product or service
value equation -- value = benefits/price (money, time, effort, etc.)
competitive advantage -- The benefit for consumers and/or customers which competitors may find difficult or uneconomic to replicate
global industry -- competitive advantage can be achieved by integrating and leveraging operations on a worldwide scale
focus -- the concentration of attention on a core business or competence
global marketing strategy (GMS) --
global market participation -- the extent to which a company has operations in major world markets
ethnocentric orientation -- person/persons who assumes that his or her home country is superior to the rest of the world
polycentric orientation -- an attitude or outlook that describes management belief or assumption that each country in which he company does business is unique
regiocentric and geocentric orientations -- a re-geocentric focuses on a region and geocentric views the entire world as a potential market and strives to develop integrated world market strategies
Global marketing -- involves an understanding of specific concepts, considerations, and strategies that must be skillfully applied in conjunction with Universal marketing fundamentals to ensure success in global markets
value chain -- The set of activities required to design, procure, produce, market, distribute, and service a product or service
value equation -- value = benefits/price (money, time, effort, etc.)
competitive advantage -- The benefit for consumers and/or customers which competitors may find difficult or uneconomic to replicate
global industry -- competitive advantage can be achieved by integrating and leveraging operations on a worldwide scale
focus -- the concentration of attention on a core business or competence
global marketing strategy (GMS) --
global market participation -- the extent to which a company has operations in major world markets
ethnocentric orientation -- person/persons who assumes that his or her home country is superior to the rest of the world
polycentric orientation -- an attitude or outlook that describes management belief or assumption that each country in which he company does business is unique
regiocentric and geocentric orientations -- a re-geocentric focuses on a region and geocentric views the entire world as a potential market and strives to develop integrated world market strategies
introduction to global marketing
Chapter 1
A company that engages in global marketing focuses its resources on global marketing opportunities and threats. Successful global marketers such as Nestlé, Coca-Cola, and Honda use familiar marketing mix elements (the four P's) to create global marketing programs. Marketing, R&D, manufacturing, and other activities comprise a firm's value chain; firms can figure these activities to create superior customer value on a global basis. Global companies also maintain strategic focus while relentlessly pursuing competitive advantage. The marketing mix, value chain, competitive advantage, and focus are universal in their applicability, irrespective of whether a company does business only in the home country or has a presence in many markets around the world. However, in a global industry, companies that fail to pursue global opportunities risk being pushed aside by stronger global competitors.
A firm's global marketing strategy (GMS) can enhance its worldwide performance. The GMS addresses several issues. First is the nature of the marketing program in terms of the balance between a standardization (extension) approach to the marketing mix elements in the localization (adaptation) approach that is responsive to country or regional differences. Second is the concentration of marketing activities in a few countries or the dispersal of such activities across many countries. Third, the pursuit of global marketing opportunities requires cross-border coordination of marketing activities. Finally, a firm's GMS will address the issue of global market participation.
The importance of global marketing today can be seen in the company rankings compiled by the Wall Street Journal, Fortune, financial Times, and other publications. Whether ranked by revenues, market capitalization, or some other measure, most of the world's major corporations are active regionally or globally. The size of global markets for individual industries or product categories helps explain why companies "go global." Global markets for some product categories represent hundreds of billions of dollars in annual sales; other markets are much smaller. Whenever the size of the opportunity, successful industry competitors find that increasing revenues and profits means seeking markets outside the home country.
Company management to be classified in terms of its orientation toward the world: ethnocentric, polycentric, regiocentric, or geocentric. An ethnocentric orientation characterized as domestic and international companies;international companies pursue marketing opportunities outside the market by extending various elements of the marketing mix. A polycentric worldview predominates at a multinational company, where the marketing mix is adapted by country managers operating at autonomously. Managers at global and transnational companies are regio centric or geocentric in their orientation and pursue both extension and adaptation strategies in global markets.
Global marketings importance today is shaped by the dynamic interplay of several driving and restraining forces.
Driving forces include:
A company that engages in global marketing focuses its resources on global marketing opportunities and threats. Successful global marketers such as Nestlé, Coca-Cola, and Honda use familiar marketing mix elements (the four P's) to create global marketing programs. Marketing, R&D, manufacturing, and other activities comprise a firm's value chain; firms can figure these activities to create superior customer value on a global basis. Global companies also maintain strategic focus while relentlessly pursuing competitive advantage. The marketing mix, value chain, competitive advantage, and focus are universal in their applicability, irrespective of whether a company does business only in the home country or has a presence in many markets around the world. However, in a global industry, companies that fail to pursue global opportunities risk being pushed aside by stronger global competitors.
A firm's global marketing strategy (GMS) can enhance its worldwide performance. The GMS addresses several issues. First is the nature of the marketing program in terms of the balance between a standardization (extension) approach to the marketing mix elements in the localization (adaptation) approach that is responsive to country or regional differences. Second is the concentration of marketing activities in a few countries or the dispersal of such activities across many countries. Third, the pursuit of global marketing opportunities requires cross-border coordination of marketing activities. Finally, a firm's GMS will address the issue of global market participation.
The importance of global marketing today can be seen in the company rankings compiled by the Wall Street Journal, Fortune, financial Times, and other publications. Whether ranked by revenues, market capitalization, or some other measure, most of the world's major corporations are active regionally or globally. The size of global markets for individual industries or product categories helps explain why companies "go global." Global markets for some product categories represent hundreds of billions of dollars in annual sales; other markets are much smaller. Whenever the size of the opportunity, successful industry competitors find that increasing revenues and profits means seeking markets outside the home country.
Company management to be classified in terms of its orientation toward the world: ethnocentric, polycentric, regiocentric, or geocentric. An ethnocentric orientation characterized as domestic and international companies;international companies pursue marketing opportunities outside the market by extending various elements of the marketing mix. A polycentric worldview predominates at a multinational company, where the marketing mix is adapted by country managers operating at autonomously. Managers at global and transnational companies are regio centric or geocentric in their orientation and pursue both extension and adaptation strategies in global markets.
Global marketings importance today is shaped by the dynamic interplay of several driving and restraining forces.
Driving forces include:
- needs and wants
- technology
- transportation and communication improvements
- product costs
- quality
- world economic trends
- opportunity recognition to develop leverage by operating globaly
- market differences
- management myopia
- organizational culture
- national controls such as non-tariff barriers
Saturday, June 09, 2007
unit 4 summary
| Marketing Data Analysis |
| Marketing research is a systematic process used to identify and solve marketing-related problems and issues. Addressing the research questions or hypotheses, however, requires that the researcher engage in data analysis. We begin our focus on analysis by examining basic analytical procedures, variance and covariance analysis, correlation and regression, and discriminant analysis. Data Analysis Researchers typically conduct a preliminary analysis of the data before conducting in-depth data analysis. Such analysis helps to provide a basic understanding of and insight into the data. In fact, many marketing research projects do not go beyond basic data analysis. Basic analysis helps us understand data distribution and allows us to test for differences or associations between two means or two medians, but what happens if our research involves multiple variables of interest? The standard statistical tests for differences between more than two means are analyses of variance and covariance. Analysis of Variance and Covariance Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) are used to examine the differences in the mean values of the dependent variable associated with the effect of the controlled independent variables. Essentially, ANOVA is used as a test of means for two or more populations. The null hypothesis, typically, is that all means all equal. For example, suppose a researcher was interested in examining whether heavy, medium, light, or non-users of cereals differed in their preference for Cereal A, measured on a nine-point scale. The null hypothesis that the four groups were not different in preference could be tested using ANOVA. If the set of independent variables, however, consisted of both categorical and metric variables, a researcher is likely to employ ANCOVA. For example, ANCOVA is useful if a researcher wanted to examine the preference of product use groups and loyalty groups, taking into account the respondents¡¦ attitudes towards nutrition and the importance they attached to breakfast as a meal. The last two variables can be measured on a Likert scale (metric), but both product use and brand loyalty represent categorical variables. Correlation and Regression Regression analysis is widely used to explain variation in market share, sales, brand preference, and other marketing results. In fact, we are often interested in summarizing the strength of association between two metric variables, as in the following situations: 1. How strongly are sales related to advertising expenditures? 2. Is there an association between market share and the size of the sales force? 3. Are consumers' perceptions of quality related to their perception of prices? The single most commonly used test of association between two metric variables is the Pearson product moment correlation, which demonstrates the strength of the linear relationship between the tested variables. Understanding the product moment correlation is critical in that it acts as the foundation for all correlation testing, including multiple regression techniques. Regression analyses are powerful and flexible procedures to analyze associative relationships between a metric dependent variable and one or more independent variables. Discriminant Analysis Finally, discriminant analysis is a technique for analyzing data for which the criterion or dependent variable is categorical, but the predictors or independent variables are interval in nature. In fact, examples of discriminant analysis abound in marketing research. This technique can be used to answer questions such as: 1. In terms of demographic characteristics, how do customers who exhibit store loyalty differ from those who do not? 2. Do the various market segments differ in their media consumption habits? 3. What are the distinguishing characteristics of consumers who respond to direct mail solicitations? |
discriminant analysis
Chapter 18
Discriminate analysis is useful for analyzing data when the criterion or dependent variable is categorical and a predictor or independent variables are interval scaled. When the criterion variable has two categories, the technique is known as two-group discriminate analysis. Multiple discriminate analysis refers to the case when three or more categories are involved.
Conducting discriminate analysis is a five step procedure:
In multiple discriminate analysis, if there are G groups and k predictors, it is possible to estimate up to the smaller of G - 1 or k discriminate functions. The first function has the highest ratio of between group to within group sums of squares. The second function, uncorrelated with the first, has the second highest ratio, and so on.
Discriminate analysis -- a technique for analyzing marketing research data when the criterion or dependent variable is categorical and the predictor or independent variables are interval in nature
discriminate functions -- the linear combination of independent variables developed by discriminate analysis that will best discriminate between the categories of the dependent variable
two-group discriminate analysis -- discriminate analysis technique where the criterion variable has two categories
multiple discriminate analysis -- discriminate analysis technique where the criterion variable involves three or more categories
discriminate analysis model -- the statistical model on which discriminate analysis is based
analysis sample -- part of the total sample that is used for estimation of the discriminate function
validation sample -- that part of the total sample used to check the results of the estimation sample
direct method -- an approach to discriminate analysis that involves estimating the discriminate function so that all the predictors are included simultaneously
stepwise discriminate analysis -- discriminate analysis in which the predictors are entered sequentially based on their ability to discriminate between the groups
characteristic profile -- an aide to interpreting discriminate analysis results by describing each group in terms of the group means for the predictor variables
hit ratio -- the percentage of cases correctly classified by the discriminate analysis
territorial map -- a tool for assessing discriminate analysis results that plots the group membership of each case on a graph
Mahalanobis procedure -- a stepwise procedure used in discriminate analysis to maximize a generalized measure of the distance between the two closest groups
Discriminate analysis is useful for analyzing data when the criterion or dependent variable is categorical and a predictor or independent variables are interval scaled. When the criterion variable has two categories, the technique is known as two-group discriminate analysis. Multiple discriminate analysis refers to the case when three or more categories are involved.
Conducting discriminate analysis is a five step procedure:
- first, formulating the discriminate problem requires identification of the objectives and the criterion and the predictor variables. The sample is divided into two parts. One part, the analysis sample, is used to estimate the discriminate function. The other part, the holdout sample, is reserved for validation.
- Estimation, the second step, involves developing a linear combination of the predictors, called discriminate functions, said that the groups differ as much as possible on the predictor values.
- Determination of statistical significance is the third step. It involves testing the null hypothesis that, in the population, the means of all discriminate functions in all groups are equal. If the null hypothesis is rejected, it is meaningful to interpret the results.
- The fourth step, the interpretation of discriminate weights or coefficients, it's similar to that in multiple regression analysis. Given the multicollinearity in the predictor variables, there is no unambiguous measure of the relative importance of the predictors and discriminating between the groups. However, some idea of the relative importance of the variables may be obtained by examining the absolute magnitude of the standardize discriminate function coefficients and by examining the structure correlations or discriminate loadings. These simple correlations between each predictor and the discriminate function represent the variance at the predictor shares with the function. Another aide to interpreting discriminate analysis results is to develop a characteristic profile for each group, based on the group means for the predictor variables.
- Validation, the fifth step, involves developing the classification matrix. The discriminate weights estimated by using the analysis sample are multiplied by the values of the predictor variables in the holdout sample to generate discriminate scores for the cases in the holdout sample. The cases are then assigned to groups based on their discriminate scores and an appropriate decision role. The percentage of cases correctly classified as determined and compared to the rate that would be expected by chance classification.
In multiple discriminate analysis, if there are G groups and k predictors, it is possible to estimate up to the smaller of G - 1 or k discriminate functions. The first function has the highest ratio of between group to within group sums of squares. The second function, uncorrelated with the first, has the second highest ratio, and so on.
Discriminate analysis -- a technique for analyzing marketing research data when the criterion or dependent variable is categorical and the predictor or independent variables are interval in nature
discriminate functions -- the linear combination of independent variables developed by discriminate analysis that will best discriminate between the categories of the dependent variable
two-group discriminate analysis -- discriminate analysis technique where the criterion variable has two categories
multiple discriminate analysis -- discriminate analysis technique where the criterion variable involves three or more categories
discriminate analysis model -- the statistical model on which discriminate analysis is based
analysis sample -- part of the total sample that is used for estimation of the discriminate function
validation sample -- that part of the total sample used to check the results of the estimation sample
direct method -- an approach to discriminate analysis that involves estimating the discriminate function so that all the predictors are included simultaneously
stepwise discriminate analysis -- discriminate analysis in which the predictors are entered sequentially based on their ability to discriminate between the groups
characteristic profile -- an aide to interpreting discriminate analysis results by describing each group in terms of the group means for the predictor variables
hit ratio -- the percentage of cases correctly classified by the discriminate analysis
territorial map -- a tool for assessing discriminate analysis results that plots the group membership of each case on a graph
Mahalanobis procedure -- a stepwise procedure used in discriminate analysis to maximize a generalized measure of the distance between the two closest groups
correlation and regression
Chapter 17
The product moment correlation coefficient, r , measures the linear association between two metric (interval or ratio scaled) variables. It's square,r2, measures the proportion of variation and one variable explained by the other. The partial correlation coefficient measures the association between two variables after controlling, or adjusting for, the affects of one or more additional variables. The order of the partial correlation indicates how many variables are being adjusted or controlled. Partial correlations can be very helpful for detecting spurious relationships.
Bivariate regression derives a mathematical equation between a single metric criterion variable and a single metric predictor variable. The equation is derived in the form of a straight line by using the least squares procedure. When the regression is run on standardized data, the intercept assumes a value of 0, and the regression coefficients are called beta weights. The strength of association is measured by the coefficient of determination, r2, which is obtained by computing a ratio of SSreg to SSy. The standard error of estimate is used to access the accuracy of prediction and may be interpreted as a kind of average error made in predicting Y from the regression equation.
Multiple regression involves a single dependent variable and to a more independent variables. The partial regression coefficient, b1 , represents the expected change in Y when X1 is changed by one unit and X2 through Xk are held constant. The strength of association is measured by the coefficient of multiple determination, R2. The significance of the overall regression equation may be tested by the overall F test. Individual partial regression coefficients may be tested for significance using the t test or the incremental F test. Scattergrams of the residuals, in which the residuals are plotted against the predicted values, time, or predictor variables, are useful for examining the appropriateness of the underlying assumptions and the regression model fitted.
In stepwise regression, the predictor variables are entered or renewed from the regression equation 1 at a time for the purpose of selecting a smaller subset of predictors that account for most of the variation in the criterion variable. Multicollinearity, or very high intercorrelations among the predictor variables, can result in several problems. Because the predictors are correlated, regression analysis provides no unambiguous measure of relative importance of the predictors. Cross validation examines whether the regression model continues to hold true for comparable data not used in estimation. It is a useful procedure for evaluating the regression model.
Nominal or categorical variables may be used as predictors by coding them as dummy variables. Multiple regression with dummy variables provide a general procedure for the analysis of variance and covariance.
Product moment correlation (r) -- a statistic summarizing the strength of association between two metric variables
covariance -- a systematic relationship between two variables in which a change in one implies a corresponding change in the other
partial correlation coefficient -- a measure of the association between two variables after controlling or adjusting for the effects of one or more additional variables
part correlation coefficient -- a measure of the correlation between Y and X when the linear affects of the other independent variables have been removed from X but not from Y
nonmetric correlation -- a correlation measure for two nonmetric variables that relies on rankings to compute the correlation
regression analysis -- a statistical procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables
bivariate regression -- a procedure for deriving a mathematical relationship, in the form of an equation, between a single metric dependent variable in a single metric independent variable
least-squares procedure -- a technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line
multiple regression -- a statistical technique that simultaneously developed a mathematical relationship between two or more independent variables and on interval scale dependent variable
multiple regression model -- an equation used to explain the results of multiple regression analysis
residual -- the difference between the observed value of Yi and the value predicted by the regression equation ,Yi
stepwise regression -- a regression procedure in which the predictor variables enter or leave the regression equation when a time
multicollinearity -- a state of very high is intercorrelations among independent variables
Cross validation -- a test of validity that examines whether a model holds on comparable data not used in the original estimation
double cross validation -- a special form of validation in which the sample is split into halves. One half serves as the estimation sample in the other as a validation sample. The roles of the estimation and validation halves and then reversed, and the cross validation process repeated
The product moment correlation coefficient, r , measures the linear association between two metric (interval or ratio scaled) variables. It's square,r2, measures the proportion of variation and one variable explained by the other. The partial correlation coefficient measures the association between two variables after controlling, or adjusting for, the affects of one or more additional variables. The order of the partial correlation indicates how many variables are being adjusted or controlled. Partial correlations can be very helpful for detecting spurious relationships.
Bivariate regression derives a mathematical equation between a single metric criterion variable and a single metric predictor variable. The equation is derived in the form of a straight line by using the least squares procedure. When the regression is run on standardized data, the intercept assumes a value of 0, and the regression coefficients are called beta weights. The strength of association is measured by the coefficient of determination, r2, which is obtained by computing a ratio of SSreg to SSy. The standard error of estimate is used to access the accuracy of prediction and may be interpreted as a kind of average error made in predicting Y from the regression equation.
Multiple regression involves a single dependent variable and to a more independent variables. The partial regression coefficient, b1 , represents the expected change in Y when X1 is changed by one unit and X2 through Xk are held constant. The strength of association is measured by the coefficient of multiple determination, R2. The significance of the overall regression equation may be tested by the overall F test. Individual partial regression coefficients may be tested for significance using the t test or the incremental F test. Scattergrams of the residuals, in which the residuals are plotted against the predicted values, time, or predictor variables, are useful for examining the appropriateness of the underlying assumptions and the regression model fitted.
In stepwise regression, the predictor variables are entered or renewed from the regression equation 1 at a time for the purpose of selecting a smaller subset of predictors that account for most of the variation in the criterion variable. Multicollinearity, or very high intercorrelations among the predictor variables, can result in several problems. Because the predictors are correlated, regression analysis provides no unambiguous measure of relative importance of the predictors. Cross validation examines whether the regression model continues to hold true for comparable data not used in estimation. It is a useful procedure for evaluating the regression model.
Nominal or categorical variables may be used as predictors by coding them as dummy variables. Multiple regression with dummy variables provide a general procedure for the analysis of variance and covariance.
Product moment correlation (r) -- a statistic summarizing the strength of association between two metric variables
covariance -- a systematic relationship between two variables in which a change in one implies a corresponding change in the other
partial correlation coefficient -- a measure of the association between two variables after controlling or adjusting for the effects of one or more additional variables
part correlation coefficient -- a measure of the correlation between Y and X when the linear affects of the other independent variables have been removed from X but not from Y
nonmetric correlation -- a correlation measure for two nonmetric variables that relies on rankings to compute the correlation
regression analysis -- a statistical procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables
bivariate regression -- a procedure for deriving a mathematical relationship, in the form of an equation, between a single metric dependent variable in a single metric independent variable
least-squares procedure -- a technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line
multiple regression -- a statistical technique that simultaneously developed a mathematical relationship between two or more independent variables and on interval scale dependent variable
multiple regression model -- an equation used to explain the results of multiple regression analysis
residual -- the difference between the observed value of Yi and the value predicted by the regression equation ,Yi
stepwise regression -- a regression procedure in which the predictor variables enter or leave the regression equation when a time
multicollinearity -- a state of very high is intercorrelations among independent variables
Cross validation -- a test of validity that examines whether a model holds on comparable data not used in the original estimation
double cross validation -- a special form of validation in which the sample is split into halves. One half serves as the estimation sample in the other as a validation sample. The roles of the estimation and validation halves and then reversed, and the cross validation process repeated
analysis of variance and covariance
Chapter 16
In ANOVA and ANCOVA, the dependent variable is metric in the independent variables are all categorical, or combinations of categorical and metric variables. One way ANOVA involves a single independent categorical variable. Interest lies in testing the null hypothesis that the category means are equal in the population. The total variation and the dependent variable is decomposed into two components: variation related to the independent variable and variation related to error. The variation is measured in terms of the sum of squares corrected for the mean (SS). The mean square is obtained by dividing the SS by the corresponding degrees of freedom (df). The null hypothesis of equal means is tested by an F statistic, which is the ratio of the mean square related to the independent variable to the mean square related to error.
N-way analysis of variance involves a simultaneous examination of two or more categorical independent variables. A major advantage is that the interactions between the independent variables can be examined. The significance of the overall effect, interaction terms, and main effects of individual factors are examined by appropriate F tests. It is meaningful to test the significance of main effects only of the corresponding interaction terms are not significant.
ANCOVA includes at least one categorical independent variable and at least one animal or metric independent variable. The metric independent variable, or covariate, is commonly used remove extraneous variation from the dependent variable.
When the analysis of variance is conducted on two or more factors, interactions can arise. And interaction occurs when the effective one independent variable on the dependent variable is different for different categories or levels of another independent variable. If the interaction is significant, it may be ordinal or disordinal. Disordinal an action may be of a non-crossover or crossover type. In balanced designs, the relative importance of factors in explaining the variation in the dependent variable is measured by omega squared. Multiple comparisons in the form of a priori or a posteriori contrasts can be used for examining differences among specific means.
And repeated measures analysis of variance, observations on each subject are obtained under each treatment condition. This design is useful for controlling the differences in subjects that exist prior to the experiment. Not metric analysis of variance involves examining the differences in the central tendencies of two or more groups when the dependent variable is measured on a ordinal scale. Multivariate analysis of variance (MANOVA) involves two or more metric dependent variables.
Analysis of variance (ANOVA) -- a statistical technique for examining the differences among means for two more populations
factors -- categorical independent variables. The independent variables must be all categorical (nonmetric) to use ANOVA
treatment -- in ANOVA, a particular combination of factor levels or categories
one-way analysis of variance -- an ANOVA technique in which there is only one factor
n-way analysis of variance -- an ANOVA model where two or more factors are involved
analysis of covariance (ANCOVA) -- an advanced analysis of variance procedure in which the effects of one or more metric scaled extraneous variables are renewed from the dependent variable before conducting the ANOVA
covariate -- and metric independent variable used in ANOVA
decomposition of the total variation -- in one-way ANOVA, separation of the variation observed in the dependent variable into the variation due to the independent variables plus the variation due to error
interaction -- when assessing the relationship between two variables, and interaction occurs if the effects of X1 depends on the level of X2, and vice versa
significance of the overall effect -- a test that some differences exist between some of the treatment groups
significance of interaction effect -- a test of the significance of the interaction between two or more independent variables
significance of the main effect -- a test of the significance of the main effect for each individual factor
ordinal interaction -- and interaction where the rank order of the effects attributable to one factor does not change across the levels of the second factor
disordinal interaction -- a change in the rank order of the effects of one factor across the levels of another
omega squared -- a measure indicating the proportion of the variation in the dependent variable explained by a particular independent variable or factor
contrasts -- in ANOVA, a method of examining differences among two or more means of the treatment groups
a priori contrasts -- contrasts that are determined before conducting the analysis, based on the researcher's theoretical framework
a postpriori contrasts -- contrast made after the analysis. These are generally multiple comparison tests
multiple comparison test -- a postpriori contrast that enable the researcher to construct generalized confidence intervals they can be used to make pairwise comparisons of all treatment means
repeated measures ANOVA -- an ANOVA technique used when respondents are exposed to more than one treatment condition and repeated measurements are obtained
nonmetric ANOVA -- an ANOVA technique for examining the difference in the central tendencies of more than two groups when the dependent variable is measured on an ordinal scale
k-sample median test -- non-parametric test that is used to examine differences among groups when the dependent variable is measured on an ordinal scale
Kruskal-Wallis one-way analysis of variance -- a nonmetric ANOVA test that uses the rank value of each case, not merely its location relative to the median
multivariate analysis of variance (MANOVA) -- an ANOVA technique using two or more metric dependent variables
In ANOVA and ANCOVA, the dependent variable is metric in the independent variables are all categorical, or combinations of categorical and metric variables. One way ANOVA involves a single independent categorical variable. Interest lies in testing the null hypothesis that the category means are equal in the population. The total variation and the dependent variable is decomposed into two components: variation related to the independent variable and variation related to error. The variation is measured in terms of the sum of squares corrected for the mean (SS). The mean square is obtained by dividing the SS by the corresponding degrees of freedom (df). The null hypothesis of equal means is tested by an F statistic, which is the ratio of the mean square related to the independent variable to the mean square related to error.
N-way analysis of variance involves a simultaneous examination of two or more categorical independent variables. A major advantage is that the interactions between the independent variables can be examined. The significance of the overall effect, interaction terms, and main effects of individual factors are examined by appropriate F tests. It is meaningful to test the significance of main effects only of the corresponding interaction terms are not significant.
ANCOVA includes at least one categorical independent variable and at least one animal or metric independent variable. The metric independent variable, or covariate, is commonly used remove extraneous variation from the dependent variable.
When the analysis of variance is conducted on two or more factors, interactions can arise. And interaction occurs when the effective one independent variable on the dependent variable is different for different categories or levels of another independent variable. If the interaction is significant, it may be ordinal or disordinal. Disordinal an action may be of a non-crossover or crossover type. In balanced designs, the relative importance of factors in explaining the variation in the dependent variable is measured by omega squared. Multiple comparisons in the form of a priori or a posteriori contrasts can be used for examining differences among specific means.
And repeated measures analysis of variance, observations on each subject are obtained under each treatment condition. This design is useful for controlling the differences in subjects that exist prior to the experiment. Not metric analysis of variance involves examining the differences in the central tendencies of two or more groups when the dependent variable is measured on a ordinal scale. Multivariate analysis of variance (MANOVA) involves two or more metric dependent variables.
Analysis of variance (ANOVA) -- a statistical technique for examining the differences among means for two more populations
factors -- categorical independent variables. The independent variables must be all categorical (nonmetric) to use ANOVA
treatment -- in ANOVA, a particular combination of factor levels or categories
one-way analysis of variance -- an ANOVA technique in which there is only one factor
n-way analysis of variance -- an ANOVA model where two or more factors are involved
analysis of covariance (ANCOVA) -- an advanced analysis of variance procedure in which the effects of one or more metric scaled extraneous variables are renewed from the dependent variable before conducting the ANOVA
covariate -- and metric independent variable used in ANOVA
decomposition of the total variation -- in one-way ANOVA, separation of the variation observed in the dependent variable into the variation due to the independent variables plus the variation due to error
interaction -- when assessing the relationship between two variables, and interaction occurs if the effects of X1 depends on the level of X2, and vice versa
significance of the overall effect -- a test that some differences exist between some of the treatment groups
significance of interaction effect -- a test of the significance of the interaction between two or more independent variables
significance of the main effect -- a test of the significance of the main effect for each individual factor
ordinal interaction -- and interaction where the rank order of the effects attributable to one factor does not change across the levels of the second factor
disordinal interaction -- a change in the rank order of the effects of one factor across the levels of another
omega squared -- a measure indicating the proportion of the variation in the dependent variable explained by a particular independent variable or factor
contrasts -- in ANOVA, a method of examining differences among two or more means of the treatment groups
a priori contrasts -- contrasts that are determined before conducting the analysis, based on the researcher's theoretical framework
a postpriori contrasts -- contrast made after the analysis. These are generally multiple comparison tests
multiple comparison test -- a postpriori contrast that enable the researcher to construct generalized confidence intervals they can be used to make pairwise comparisons of all treatment means
repeated measures ANOVA -- an ANOVA technique used when respondents are exposed to more than one treatment condition and repeated measurements are obtained
nonmetric ANOVA -- an ANOVA technique for examining the difference in the central tendencies of more than two groups when the dependent variable is measured on an ordinal scale
k-sample median test -- non-parametric test that is used to examine differences among groups when the dependent variable is measured on an ordinal scale
Kruskal-Wallis one-way analysis of variance -- a nonmetric ANOVA test that uses the rank value of each case, not merely its location relative to the median
multivariate analysis of variance (MANOVA) -- an ANOVA technique using two or more metric dependent variables
frequency distribution, cross tabulation, and hypothesis testing
Chapter 15
Basic data analysis provides viable insights and guides the rest of the data analysis as well as the interpretation of the results. A frequency distribution should be obtained for each variable in the data. This analysis produces a table of frequency counts, percentages, and cumulative percentages for all the values associated with that variable. It indicates the extent of out of range, missing, or extreme values. The mean, mode, and median of a frequency distribution are measures of central tendency. The variability of the distribution is described by the range, the variants or standard deviation, coefficient of variation, and interquartile range. Skewness and kurtosis provide an idea of the shape of the distribution.
Cross tabulations are tables that reflect the joint distribution of two or more variables. In cross tabulation, the percentages can be computed either column wise, based on column totals, or row wise, based on row totals. The general rule is to compete the percentages in the direction of the independent variable, across the dependent variable. Often the introduction of a third variable can't provide additional insights. The Chi Square statistic provides a test of the statistical significance of the observed association in a cross tabulation. The phi coefficient, contingency coefficient, Cramer's V, and the lambda coefficient provide measures of the strength of association between the variables.
Parametric and non-parametric tests are available for testing hypothesis related to differences. And the parametric case, the t test is used to examine hypotheses related to the population mean. Different forms of the t test are suitable for testing hypotheses based on one sample, two independent samples, or paired samples. In the nonparametric case, popular one sample tests include the Kolmogorov-Smirnov, chi-square, runs test, and the binomial test. For two independent nonparametric samples, the Mann-Whitney U test, median test and the Kolmogorov-Smirnov test can be used. For paired samples, the Wlicoxon matched-pairs signed-ranks test and assign tests are useful for examining hypotheses related to measures of location.
frequency distribution -- a mathematical distribution whose objective is to obtain a count of the number of responses associated with different values of one variable and to express these counts in percentage terms
measures of location -- a statistic that describes a location within a data set. Measures of central tendency described the center of the distribution
mean -- the average; that value obtained by summing all elements in a set and dividing by the number of elements
mode -- a measure of central tendency given as the value that occurs the most in a sample distribution
median -- a measure of central tendency given as the value above which half of the values fall and below which half of the values fall
measures of variability -- a statistic that indicates the distributions dispersion
range -- the difference between the largest and smallest values of distribution
interquartile range -- the range of distribution income passing the middle 50% of the observations
variants -- the mean squared deviation of all the values from the mean
standard deviation -- the square root of the variance
coefficient of variation -- a useful expression in sampling theory for the standard deviation as a percentage of the mean
skewness -- a characteristic of a distribution that assesses its symmetry about the mean
kurtosis -- a measure of the relative peakedness or flatness of the curve defined by the frequency distribution
null hypothesis -- a statement in which no difference or effect is expected. If the null hypothesis is not rejected, no changes will be made
alternative hypothesis -- a statement that some difference or effect is expected. Excepting the alternative hypothesis will lead to changes in opinions or actions
one tailed test -- a test of the null hypothesis where the alternative hypothesis is expressed directionally
two tailed test -- a test of the null hypothesis where the alternative hypothesis is not expressed directionally
test statistic -- a measure of how close the sample has come to the null hypothesis. It often follows a well-known distribution, such as the normal, t, or chi- squared distribution
type I error -- also known as Alpha error, occurs when a sample results lead to the rejection of a null hypothesis that is in fact true
level of significance -- the probability of making a type 1 error
type II error -- also known as beta error, occurs when the sample results lead to the non-rejection of a null hypothesis that is in fact false
power of a test -- the probability of rejecting the null hypothesis when it is in fact false and should be rejected
Cross tabulation -- a statistical technique that describes two or more variables simultaneously and results in tables that reflect the joint distribution of two or more variables that have a limited number of categories or distinct values
contingency table -- a cross tabulation table. It contains a cell for every combination of categories of the two variables
chi-square statistic -- the statistic used to test the statistical significance of the observed association and cross tabulation. It assists us in determining whether a systematic association exists between the two variables
chi-square distribution -- a skewed distribution and shape depends solely on the number of degrees of freedom. As the number of degrees of freedom increases, the chi-square distribution becomes more symmetrical
phi coefficient -- a measure of the strength of Association and the special case of a table with two rows and two columns
contingency coefficient (C) -- a measure of the strength of association in a table of any size
Cramer's V -- a measure of the strength of association used in tables larger than 2 x 2
asymmetric lambda -- a measure of the percentage improvement in predicting the value of the dependent variable, given the value of the independent variable and contingency table analysis. Lambda also varies between zero and one
symmetric lambda -- the symmetric lambda does not make an assumption about which variable is dependent. It measures the overall improvement when production is done in both directions
tau b -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is square
tau c -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is not square but a rectangle
Gamma -- test statistic that measures the association between two ordinal-level variables. It does not make an adjustment for ties
parametric tests -- hypothesis testing procedures that assume that the variables of interest are measured on at least an interval scale
non-parametric tests -- hypothesis testing procedures that assume that the variables are measured on a nominal or ordinal scale
t test -- a univariate hypothesis test using the t distribution, which is used in the standard deviation is unknown and the sample size is small
t statistic -- a statistic that assumes that the variable has a symmetric bell shaped distribution in the mean is known (or assumed to be known) and the population variants is estimated from the sample
t distribution -- symmetric bell shaped distribution that is useful for small sample testing
z test -- a univariate hypothesis test using the standard normal distribution
independent samples -- to samples that are not experimentally related. The measurement of one sample has no effect on the values of the second sample
f test -- a statistical test of the equality of the variances of two populations
f statistic -- the f statistic is computed as the ratio of two sample variances
f distribution -- a frequency distribution that depends on two sets of degrees of freedom -- the degrees of freedom in the numerator and the degrees of freedom in the denominator
paired samples -- and hypothesis testing, the observations are paired so that two sets of observations relate to the same respondents
paired samples t test -- a test for differences in the means of paired samples
Kolmogorov-Smirnov one-sample test - A one sample nonparametric goodness of fit test that compares the cumulative distribution function for a variable with a specified distribution
runs test -- a test of randomness for a dichotomous variable
binomial test -- a goodness of fit statistical test for dichotomous variables. It tests the goodness of fit of the observed number of observations in each category to the number expected under a specified binomial distribution
Mann-Whitney U test -- a statistical test for the variable measured on an ordinal scale comparing the difference in the location of two populations based on observations from two independent samples
two-sample median test -- non-parametric test statistic that determines whether two groups are drawn from populations with the same median. This test is not as powerful as the Mann- Whitney U
Kolmogorov-Smirnov two-sample test -- nonparametric test statistic that determines whether to his divisions are the same. It takes into account any differences in the two distributions including median, dispersion, and skewness
Wilcoxon matched-pairs signed-ranks test -- a nonparametric test that analyzes the differences between the paired observations, taking into account the magnitude of the differences
sign test -- a nonparametric test for examining differences in the location of two populations, based on paired observations, that compares only the signs of the differences between pairs of variables without taking into account the magnitude of the differences
Basic data analysis provides viable insights and guides the rest of the data analysis as well as the interpretation of the results. A frequency distribution should be obtained for each variable in the data. This analysis produces a table of frequency counts, percentages, and cumulative percentages for all the values associated with that variable. It indicates the extent of out of range, missing, or extreme values. The mean, mode, and median of a frequency distribution are measures of central tendency. The variability of the distribution is described by the range, the variants or standard deviation, coefficient of variation, and interquartile range. Skewness and kurtosis provide an idea of the shape of the distribution.
Cross tabulations are tables that reflect the joint distribution of two or more variables. In cross tabulation, the percentages can be computed either column wise, based on column totals, or row wise, based on row totals. The general rule is to compete the percentages in the direction of the independent variable, across the dependent variable. Often the introduction of a third variable can't provide additional insights. The Chi Square statistic provides a test of the statistical significance of the observed association in a cross tabulation. The phi coefficient, contingency coefficient, Cramer's V, and the lambda coefficient provide measures of the strength of association between the variables.
Parametric and non-parametric tests are available for testing hypothesis related to differences. And the parametric case, the t test is used to examine hypotheses related to the population mean. Different forms of the t test are suitable for testing hypotheses based on one sample, two independent samples, or paired samples. In the nonparametric case, popular one sample tests include the Kolmogorov-Smirnov, chi-square, runs test, and the binomial test. For two independent nonparametric samples, the Mann-Whitney U test, median test and the Kolmogorov-Smirnov test can be used. For paired samples, the Wlicoxon matched-pairs signed-ranks test and assign tests are useful for examining hypotheses related to measures of location.
frequency distribution -- a mathematical distribution whose objective is to obtain a count of the number of responses associated with different values of one variable and to express these counts in percentage terms
measures of location -- a statistic that describes a location within a data set. Measures of central tendency described the center of the distribution
mean -- the average; that value obtained by summing all elements in a set and dividing by the number of elements
mode -- a measure of central tendency given as the value that occurs the most in a sample distribution
median -- a measure of central tendency given as the value above which half of the values fall and below which half of the values fall
measures of variability -- a statistic that indicates the distributions dispersion
range -- the difference between the largest and smallest values of distribution
interquartile range -- the range of distribution income passing the middle 50% of the observations
variants -- the mean squared deviation of all the values from the mean
standard deviation -- the square root of the variance
coefficient of variation -- a useful expression in sampling theory for the standard deviation as a percentage of the mean
skewness -- a characteristic of a distribution that assesses its symmetry about the mean
kurtosis -- a measure of the relative peakedness or flatness of the curve defined by the frequency distribution
null hypothesis -- a statement in which no difference or effect is expected. If the null hypothesis is not rejected, no changes will be made
alternative hypothesis -- a statement that some difference or effect is expected. Excepting the alternative hypothesis will lead to changes in opinions or actions
one tailed test -- a test of the null hypothesis where the alternative hypothesis is expressed directionally
two tailed test -- a test of the null hypothesis where the alternative hypothesis is not expressed directionally
test statistic -- a measure of how close the sample has come to the null hypothesis. It often follows a well-known distribution, such as the normal, t, or chi- squared distribution
type I error -- also known as Alpha error, occurs when a sample results lead to the rejection of a null hypothesis that is in fact true
level of significance -- the probability of making a type 1 error
type II error -- also known as beta error, occurs when the sample results lead to the non-rejection of a null hypothesis that is in fact false
power of a test -- the probability of rejecting the null hypothesis when it is in fact false and should be rejected
Cross tabulation -- a statistical technique that describes two or more variables simultaneously and results in tables that reflect the joint distribution of two or more variables that have a limited number of categories or distinct values
contingency table -- a cross tabulation table. It contains a cell for every combination of categories of the two variables
chi-square statistic -- the statistic used to test the statistical significance of the observed association and cross tabulation. It assists us in determining whether a systematic association exists between the two variables
chi-square distribution -- a skewed distribution and shape depends solely on the number of degrees of freedom. As the number of degrees of freedom increases, the chi-square distribution becomes more symmetrical
phi coefficient -- a measure of the strength of Association and the special case of a table with two rows and two columns
contingency coefficient (C) -- a measure of the strength of association in a table of any size
Cramer's V -- a measure of the strength of association used in tables larger than 2 x 2
asymmetric lambda -- a measure of the percentage improvement in predicting the value of the dependent variable, given the value of the independent variable and contingency table analysis. Lambda also varies between zero and one
symmetric lambda -- the symmetric lambda does not make an assumption about which variable is dependent. It measures the overall improvement when production is done in both directions
tau b -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is square
tau c -- test statistic that measures the association between two ordinal-level variables. It makes adjustment for ties and is most appropriate when the table of variables is not square but a rectangle
Gamma -- test statistic that measures the association between two ordinal-level variables. It does not make an adjustment for ties
parametric tests -- hypothesis testing procedures that assume that the variables of interest are measured on at least an interval scale
non-parametric tests -- hypothesis testing procedures that assume that the variables are measured on a nominal or ordinal scale
t test -- a univariate hypothesis test using the t distribution, which is used in the standard deviation is unknown and the sample size is small
t statistic -- a statistic that assumes that the variable has a symmetric bell shaped distribution in the mean is known (or assumed to be known) and the population variants is estimated from the sample
t distribution -- symmetric bell shaped distribution that is useful for small sample testing
z test -- a univariate hypothesis test using the standard normal distribution
independent samples -- to samples that are not experimentally related. The measurement of one sample has no effect on the values of the second sample
f test -- a statistical test of the equality of the variances of two populations
f statistic -- the f statistic is computed as the ratio of two sample variances
f distribution -- a frequency distribution that depends on two sets of degrees of freedom -- the degrees of freedom in the numerator and the degrees of freedom in the denominator
paired samples -- and hypothesis testing, the observations are paired so that two sets of observations relate to the same respondents
paired samples t test -- a test for differences in the means of paired samples
Kolmogorov-Smirnov one-sample test - A one sample nonparametric goodness of fit test that compares the cumulative distribution function for a variable with a specified distribution
runs test -- a test of randomness for a dichotomous variable
binomial test -- a goodness of fit statistical test for dichotomous variables. It tests the goodness of fit of the observed number of observations in each category to the number expected under a specified binomial distribution
Mann-Whitney U test -- a statistical test for the variable measured on an ordinal scale comparing the difference in the location of two populations based on observations from two independent samples
two-sample median test -- non-parametric test statistic that determines whether two groups are drawn from populations with the same median. This test is not as powerful as the Mann- Whitney U
Kolmogorov-Smirnov two-sample test -- nonparametric test statistic that determines whether to his divisions are the same. It takes into account any differences in the two distributions including median, dispersion, and skewness
Wilcoxon matched-pairs signed-ranks test -- a nonparametric test that analyzes the differences between the paired observations, taking into account the magnitude of the differences
sign test -- a nonparametric test for examining differences in the location of two populations, based on paired observations, that compares only the signs of the differences between pairs of variables without taking into account the magnitude of the differences
Wednesday, May 30, 2007
Sampling and Data Collection Summary
Sampling
The objective of most marketing research projects is to obtain information about the characteristics or parameters of a population. Such information may be obtained by taking either a census or sample. Budget and time limits, large population size, and small variance in the characteristic of interest most often favor the use of a sample. How is sampling performed?
The sampling design process includes five steps which are closely interrelated and relevant to all aspects of the marketing research project, from problem definition to the presentation of the results. These five steps are: defining the target population; determining the sample frame; selecting a sampling technique; determining the sample size; and executing the sampling process. Let us look at each of these more closely.
1. Defining the Target Population: The target population is the collection of elements or objects that possess the information sought by the researcher, and about which inferences are to be made
2. Determining the Sample Frame: A sample frame is a representation of the elements of the target population
3. Selecting a Sampling Technique: Selecting a sampling technique involves several decisions, such as whether to use a Bayesian or traditional sampling approach, sample with or without replacement, and use nonprobability or probability sampling
4. Determining the Sample Size: Determining the sample size involves several qualitative and quantitative considerations, such as the importance of the decision; the nature of the research; the number of variables involved; the costs involved in execution; the nature of the analysis, completion rates, statistical power, and confidence intervals
5. Executing the Sampling Process: Execution of the sampling process requires a detailed specification of how the sampling decisions are to be implemented with respect to the population; sampling frame; sampling unit; sampling technique; and sample size
Data Collection
Marketing researchers often use questionnaires to obtain quantitative primary data. A questionnaire is a set of questions, which are very carefully and purposely designed to capture as much of the information necessary for addressing the research question(s) and for minimizing response error.
Regardless of form (for example, questionnaire, structured interview, and so forth), researchers have two major options for collecting data: developing their own organizations, or contracting with field-work agencies. In either case, data collection involves the use of a field force. In projects demanding direct contact between field workers and respondents, all field workers should be trained in important aspects of the data collection process, including making the initial contact; asking the questions; probing; recording the answers; and terminating the interview. Supervision of field workers involves quality control and editing; sampling control; checks for cheating; central office control; and validity and reliability measures.
Data Preparation
The data must be prepared before any analysis can be performed. Data preparation is a meticulous process. Each questionnaire must be checked for completeness and accuracy. Incomplete, ambiguous, and inconsistent responses cannot be utilized in the analysis. The data must be coded; that is, a numeric or alphanumeric code is assigned to specific responses to each specific question. The coded data is then entered into a computer system and further treated for missing values. Options available for treating missing responses include substitution of a neutral value, such as the arithmetic mean; substitution of an imputed response; casewise deletion; and pairwise deletion. Data can also be statistically adjusted in an effort to enhance the quality of data analysis. Adjustment procedures include weighting, variable respecification, and scale transformations.
data preparation
Chapter 14
Data preparation begins with a preliminary check of all questionnaires for completeness and interviewing quality. Then more thorough editing takes place. Editing consists of screening questionnaires to identify illegible, incomplete, inconsistent, or ambiguous responses. Such responses may be handled by returning questionnaires to the field, assigning missing values, or discarding the unsatisfactory respondents.
The next step is coding. A new work will or alphanumeric code is assigned to represent a specific response to a specific question, along with the column position that code will occupy. It is often helpful to prepare a codebook containing decoding instructions and the necessary information about the variables in the data set. The coded data are transcribed into disks or magnetic tapes were entered into computers via key punching. Mark sense forms, optical scanning, or computerized sensory analysis may also be used.
Cleaning the data requires consistency checks and treatment of missing responses. Options are available for treating missing responses include substitution of a neutral value such as the mean, substitution of an imputed response, case lies deletion, and pairwise deletion. Statistical adjustments such as weighting, variable re-specification, and scale transformations often and enhance the quality of data analysis. The selection of a data analysis strategy should be based on the earlier steps of the marketing research process, known characteristics of the data, properties of statistical techniques, and a background in philosophy of the researcher. Statistical techniques may be classified as univariate or multivariate.
Before analyzing the data in international marketing research, the researcher should ensure that the units of measurement are comparable across countries or cultural units.
The data analysis could be conducted at three levels:
Editing -- a review of the questionnaires with the objective of increasing accuracy and precision
coding -- the assignment of a code to represent a specific response to a specific question along with the data record and column position that code will occupy
fixed-field codes -- a code in which the number of records for each respondent are the same, and the same data appear in the same columns for all respondents
codebook -- a book containing coding instructions and the necessary information about variables in the data set
data cleaning -- thorough and extensive checks for consistency and treatment of missing responses
consistency checks -- a part of the data cleaning process that identifies data that is out of range, logically inconsistent, or have extreme values. Data with values not defined by the coding scheme is inadmissible
missing responses -- values of a variable that are on men, as these respondents did not provide unambiguous answers to the question
casewise deletion -- a method for handling missing responses in which cases or respondents with any missing responses are discarded from the analysis
pairwise deletion -- a method of handling missing values in which all cases, or respondents, with any missing values are not automatically discarded, rather, for each calculation only the cases or respondents with complete responses are considered
weighting -- a statistical adjustment to the data in which each case or respondents in the database is assigned a weight to reflect its importance relative to other cases or respondents
variable respecification -- the transformation of data to create new variables or the modification of existing variables set that they are more consistent with the objectives of the study
dummy variables -- a respecification procedure using variables that take aren't only two values, usually zero or one
scale transformation -- and manipulation of scale values to ensure comparability with other scales or otherwise make the data suitable for analysis
standardization -- the process of correcting data to reduce them to the same scale by subtracting the sample mean and dividing by the standard deviation
univariate techniques -- statistical techniques appropriate for analyzing data when there is a single measurement of each element in the sample or, if there are several measurements on each element, each variable is analyzed in isolation
multivariate techniques -- statistical techniques suitable for analyzing data when there are two or more measurements on each element in the variables are analyzed simultaneously. Multivariate techniques are concerned with the simultaneous relationships among two or more phenomena
metric data -- data that is interval or ratio in nature
nonmetric data -- data derived from a nominal or ordinal scale
Independent -- the samples are independent if they are drawn randomly from different populations
paired -- the samples are paired when the data for the two samples relate to the same group of respondents
dependence techniques -- multivariate techniques appropriate when one or more of the variables can be identified as dependent variables and the remaining as independent variables
interdependence techniques -- multivariate statistical techniques that attempt to group data based on underlying similarity, and does allow for interpretation of the data structures. No distinction is made as to which variables are dependent and which are independent
intracultural analysis -- within country analysis of international data
pancultural analysis -- across countries analysis in which the data for all respondents from all the countries are pooled and analyzed
cross-cultural analysis -- a type of a cross countries analysis in which the data could be aggregated for each country and these aggregate statistics analyzed
Data preparation begins with a preliminary check of all questionnaires for completeness and interviewing quality. Then more thorough editing takes place. Editing consists of screening questionnaires to identify illegible, incomplete, inconsistent, or ambiguous responses. Such responses may be handled by returning questionnaires to the field, assigning missing values, or discarding the unsatisfactory respondents.
The next step is coding. A new work will or alphanumeric code is assigned to represent a specific response to a specific question, along with the column position that code will occupy. It is often helpful to prepare a codebook containing decoding instructions and the necessary information about the variables in the data set. The coded data are transcribed into disks or magnetic tapes were entered into computers via key punching. Mark sense forms, optical scanning, or computerized sensory analysis may also be used.
Cleaning the data requires consistency checks and treatment of missing responses. Options are available for treating missing responses include substitution of a neutral value such as the mean, substitution of an imputed response, case lies deletion, and pairwise deletion. Statistical adjustments such as weighting, variable re-specification, and scale transformations often and enhance the quality of data analysis. The selection of a data analysis strategy should be based on the earlier steps of the marketing research process, known characteristics of the data, properties of statistical techniques, and a background in philosophy of the researcher. Statistical techniques may be classified as univariate or multivariate.
Before analyzing the data in international marketing research, the researcher should ensure that the units of measurement are comparable across countries or cultural units.
The data analysis could be conducted at three levels:
- individual
- within country or cultural unit (intercultural analysis)
- across countries or cultural units: pancultural or cross cultural analysis
Editing -- a review of the questionnaires with the objective of increasing accuracy and precision
coding -- the assignment of a code to represent a specific response to a specific question along with the data record and column position that code will occupy
fixed-field codes -- a code in which the number of records for each respondent are the same, and the same data appear in the same columns for all respondents
codebook -- a book containing coding instructions and the necessary information about variables in the data set
data cleaning -- thorough and extensive checks for consistency and treatment of missing responses
consistency checks -- a part of the data cleaning process that identifies data that is out of range, logically inconsistent, or have extreme values. Data with values not defined by the coding scheme is inadmissible
missing responses -- values of a variable that are on men, as these respondents did not provide unambiguous answers to the question
casewise deletion -- a method for handling missing responses in which cases or respondents with any missing responses are discarded from the analysis
pairwise deletion -- a method of handling missing values in which all cases, or respondents, with any missing values are not automatically discarded, rather, for each calculation only the cases or respondents with complete responses are considered
weighting -- a statistical adjustment to the data in which each case or respondents in the database is assigned a weight to reflect its importance relative to other cases or respondents
variable respecification -- the transformation of data to create new variables or the modification of existing variables set that they are more consistent with the objectives of the study
dummy variables -- a respecification procedure using variables that take aren't only two values, usually zero or one
scale transformation -- and manipulation of scale values to ensure comparability with other scales or otherwise make the data suitable for analysis
standardization -- the process of correcting data to reduce them to the same scale by subtracting the sample mean and dividing by the standard deviation
univariate techniques -- statistical techniques appropriate for analyzing data when there is a single measurement of each element in the sample or, if there are several measurements on each element, each variable is analyzed in isolation
multivariate techniques -- statistical techniques suitable for analyzing data when there are two or more measurements on each element in the variables are analyzed simultaneously. Multivariate techniques are concerned with the simultaneous relationships among two or more phenomena
metric data -- data that is interval or ratio in nature
nonmetric data -- data derived from a nominal or ordinal scale
Independent -- the samples are independent if they are drawn randomly from different populations
paired -- the samples are paired when the data for the two samples relate to the same group of respondents
dependence techniques -- multivariate techniques appropriate when one or more of the variables can be identified as dependent variables and the remaining as independent variables
interdependence techniques -- multivariate statistical techniques that attempt to group data based on underlying similarity, and does allow for interpretation of the data structures. No distinction is made as to which variables are dependent and which are independent
intracultural analysis -- within country analysis of international data
pancultural analysis -- across countries analysis in which the data for all respondents from all the countries are pooled and analyzed
cross-cultural analysis -- a type of a cross countries analysis in which the data could be aggregated for each country and these aggregate statistics analyzed
data collection: fieldwork
data collection, preparation, analysis, and reporting -- Chapter 13 -- fieldwork
Researchers have two major options for collecting data: developing their own organizations or contracting with fieldwork agencies. In either case, data collection and involves the use of a field force. Field workers should be healthy, outgoing, creative, pleasant, educated, and experienced. They should be trained in important aspects of fieldwork, including making the initial contact, asking the questions, probing, recording the answers, and terminating the interview.
Supervision of field workers and involves quality control and editing, sampling control, control of cheating, and central office control. Validation of fieldwork can be accomplished by calling 10 to 25% of those who have been identified as interviewees and inquiring whether the interviews took place. Field workers should be evaluated on the basis of cost and time, response rates, quality of interviewing, and quality of data collection.
The selection, training, supervision, and evaluation of field workers is even more critical in international marketing research, as local fieldwork agencies are not available in many countries. Ethical issues include making the respondents feel comfortable in the data collection process so that their experience is positive. Every effort must be undertaken to ensure that the data is of high quality. The Internet and computers can greatly facilitate and improve the quality of fieldwork.
Probing -- a motivational technique used when asking survey questions to induce the respondents to enlarge on, clarify, or explain their answers and to help the respondents to focus on the specific content of the interview
sampling control -- an aspect of supervision that ensures that the interviewers strictly follow the sampling plan rather than select sampling units based on convenience or accessibility
Researchers have two major options for collecting data: developing their own organizations or contracting with fieldwork agencies. In either case, data collection and involves the use of a field force. Field workers should be healthy, outgoing, creative, pleasant, educated, and experienced. They should be trained in important aspects of fieldwork, including making the initial contact, asking the questions, probing, recording the answers, and terminating the interview.
Supervision of field workers and involves quality control and editing, sampling control, control of cheating, and central office control. Validation of fieldwork can be accomplished by calling 10 to 25% of those who have been identified as interviewees and inquiring whether the interviews took place. Field workers should be evaluated on the basis of cost and time, response rates, quality of interviewing, and quality of data collection.
The selection, training, supervision, and evaluation of field workers is even more critical in international marketing research, as local fieldwork agencies are not available in many countries. Ethical issues include making the respondents feel comfortable in the data collection process so that their experience is positive. Every effort must be undertaken to ensure that the data is of high quality. The Internet and computers can greatly facilitate and improve the quality of fieldwork.
Probing -- a motivational technique used when asking survey questions to induce the respondents to enlarge on, clarify, or explain their answers and to help the respondents to focus on the specific content of the interview
sampling control -- an aspect of supervision that ensures that the interviewers strictly follow the sampling plan rather than select sampling units based on convenience or accessibility
sampling: final and initial sample size determination
Chapter 12
The statistical approaches to determining sample size are based on confidence intervals. These approaches may involve the estimation of the mean or proportion. When estimating the mean, determination of sample size using a confidence interval approach requires a specification of precision level, confidence level, and population standard deviation. In the case of proportion, the precision level, confidence level, and an estimate of the population proportion must be specified. The sample size determined statistically represents the final or net sample size that must be achieved. In order to achieve this final sample size, a much greater number of potential respondents have to be contacted to account for reduction in response due to incidence rates and completion rates.
Non-response error arises when some of the potential respondents included in the sample did not respond. The primary causes of low response rates are refusals and not-at-homes. Refusal rates may be reduced by prior notification, motivating the respondents, incentives, proper questionnaire design and administration, and follow-up. The percentage of not-at-homes can be substantially reduced by callbacks. Adjustments for non-response can be made by subsampling non-respondents, replacement, substitution, subjective estimates, trend analysis, weighting, and imputation.
The statistical estimation of sample size is even more complicated in international marketing research, as the population variance may differ from one country to the next. A preliminary estimation of population variance for the purpose of determining the sample size also has ethical ramifications. The Internet and computers can assist in determining the sample size and adjusting it to a count for expected incidence and completion rates.
Sampling distribution -- the distribution of the values of a sample statistic computed for each possible sample that could be drawn from the target population under a specified sampling plan
statistical inference -- the process of generalizing the sample results to the population results
normal distribution -- a basis for classical statistical inference that is bell shaped and symmetrical and appearance. Its measures of central tendency are all identical
standard error -- the standard deviation of the sampling distribution of the mean or proportion
z values -- the number of standard errors in point is away from the mean
incidence rate -- the rate of occurrence of persons eligible to participate in a study expressed as a percentage
completion rate -- the percentage of qualified respondents to complete the interview. It enables researchers to take into account anticipated refusals by people who qualify
substitution -- a procedure that substitutes for nonrespondents other elements from the sampling frame that are expected to respond
trend analysis -- a method of adjusting for nonrespondents in which the researcher tries to discern a trend between early and late respondents. This trend is projected to nonrespondents to estimate their characteristic of interest
weighting -- statistical procedure that attempts to account for non-response by assigning differential weight to the data depending on the response rate
imputation -- a method to adjust for non-response by assigning to characteristic of interest to the nonrespondents based on the similarity of the variables available for both nonrespondents and respondents
The statistical approaches to determining sample size are based on confidence intervals. These approaches may involve the estimation of the mean or proportion. When estimating the mean, determination of sample size using a confidence interval approach requires a specification of precision level, confidence level, and population standard deviation. In the case of proportion, the precision level, confidence level, and an estimate of the population proportion must be specified. The sample size determined statistically represents the final or net sample size that must be achieved. In order to achieve this final sample size, a much greater number of potential respondents have to be contacted to account for reduction in response due to incidence rates and completion rates.
Non-response error arises when some of the potential respondents included in the sample did not respond. The primary causes of low response rates are refusals and not-at-homes. Refusal rates may be reduced by prior notification, motivating the respondents, incentives, proper questionnaire design and administration, and follow-up. The percentage of not-at-homes can be substantially reduced by callbacks. Adjustments for non-response can be made by subsampling non-respondents, replacement, substitution, subjective estimates, trend analysis, weighting, and imputation.
The statistical estimation of sample size is even more complicated in international marketing research, as the population variance may differ from one country to the next. A preliminary estimation of population variance for the purpose of determining the sample size also has ethical ramifications. The Internet and computers can assist in determining the sample size and adjusting it to a count for expected incidence and completion rates.
Sampling distribution -- the distribution of the values of a sample statistic computed for each possible sample that could be drawn from the target population under a specified sampling plan
statistical inference -- the process of generalizing the sample results to the population results
normal distribution -- a basis for classical statistical inference that is bell shaped and symmetrical and appearance. Its measures of central tendency are all identical
standard error -- the standard deviation of the sampling distribution of the mean or proportion
z values -- the number of standard errors in point is away from the mean
incidence rate -- the rate of occurrence of persons eligible to participate in a study expressed as a percentage
completion rate -- the percentage of qualified respondents to complete the interview. It enables researchers to take into account anticipated refusals by people who qualify
substitution -- a procedure that substitutes for nonrespondents other elements from the sampling frame that are expected to respond
trend analysis -- a method of adjusting for nonrespondents in which the researcher tries to discern a trend between early and late respondents. This trend is projected to nonrespondents to estimate their characteristic of interest
weighting -- statistical procedure that attempts to account for non-response by assigning differential weight to the data depending on the response rate
imputation -- a method to adjust for non-response by assigning to characteristic of interest to the nonrespondents based on the similarity of the variables available for both nonrespondents and respondents
sampling: design and procedures
Chapter 11
Information about the characteristics of a population may be obtained by conducting either a sample or a census. Budget and time limits, large population size, and small variants and a characteristic of interest favor the use of a sample. Sampling is also preferred when the cost of sampling error is low, the cost of non-sampling error is high, the nature of measurement is destructive, and attention must be focused on the individual cases. The opposite set of conditions favor the use of a census.
Sampling begins by defining the target population in terms of:
Sampling techniques may be classified as non-probability and probability techniques. Nonprobability sampling techniques rely on the researcher's judgment. Consequently, they do not permit an objective evaluation of the precision of the sample results, and the estimates obtained are not statistically projectable to the population.
The commonly used the non-probability sampling techniques include:
Probability sampling techniques include:
When conducting international marketing research, it is desirable to achieve comparability and sample composition and representativeness even though this may require the use of different sampling techniques in different countries. It is unethical and misleading to treat nonprobability samples as probability samples and project the results to the target population. The Internet and computers can be used to make the sampling design process more effective and efficient.
Population -- the aggregate of all the elements, sharing some common set of characteristics, that comprises the universe for the purpose of the marketing research problem
Census -- a complete enumeration of the elements of the population or study objects
sample -- a subgroup of the elements of the population selected for participation in the study
target population -- the collection of elements or objects that possesses the information sought by the researcher and about which inferences are to be made
element -- objects that possess the information sought by the researcher and about which inferences are to be made
sampling unit -- the basic unit containing the elements of the population to be sampled
sampling frame -- a representation of the elements of the target population. It consists of a list or set of directions for identifying the target population
Bayesian approach -- a selection method with the elements are selected sequentially. This approach explicitly incorporates prior information about population parameters as well as costs and probabilities associated with making wrong decisions
sampling with replacement -- a sampling technique in which an element can be included in the sample more than once
sampling without replacement -- a sampling technique in which an element cannot be included in the sample more than once
sample size -- the number of elements to be included in the study
nonprobability sampling -- sampling techniques that do not use chance selection procedures. Rather, they rely on a personal judgment of the researcher
probability sampling -- a sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample
convenience sampling -- a nonprobability sampling technique that attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer
judgmental sampling -- a form of convenience sampling in which the population elements are purposively based on the judgment of the researcher
quota sampling -- and nonprobability sampling technique that is a two-stage restricted judgmental sampling. The first stage consists of developing control categories or quotas of population elements. In the second stage, sample elements are selected based on convenience or judgment
Snowball sampling -- and nonprobability sampling technique in which an initial group of respondents is selected randomly. Subsequent respondents are selected based on the referrals or information provided by the initial respondents. This process may be carried out in ways by obtaining referrals from referrals
simple random sampling (SRS) -- a probability sampling technique in which each element in the population has a known and equal probability of selection. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame
systematic sampling -- a probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame
stratified sampling -- a probability sampling technique that uses a two step process to partition the population into subpopulations, or strata. Elements are selected from each stratum by a random procedure
cluster sampling -- first, the target population is divided into mutually exclusive and collectively exhaustive subpopulations called clusters. Then, a random sample of clusters is selected based on a probability sampling technique such as simple random sampling. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically
area sampling -- a common form of cluster sampling in which the clusters consist of geographic areas such as countries, housing tracts, block, or other area descriptions
probability proportionate to size sampling -- a selection method with the clusters are selected with probability proportional to size and a probability of selecting a sampling unit and a selected cluster varies inversely with the size of the cluster
sequential sampling -- a probability sampling technique in which the population elements are sampled sequentially, data collection and analysis are done at each stage, and a decision is made as to whether additional population elements should be sampled
double sampling -- a sampling technique in which certain population elements are sampled twice
Information about the characteristics of a population may be obtained by conducting either a sample or a census. Budget and time limits, large population size, and small variants and a characteristic of interest favor the use of a sample. Sampling is also preferred when the cost of sampling error is low, the cost of non-sampling error is high, the nature of measurement is destructive, and attention must be focused on the individual cases. The opposite set of conditions favor the use of a census.
Sampling begins by defining the target population in terms of:
- elements
- sampling units
- extent
- time
Sampling techniques may be classified as non-probability and probability techniques. Nonprobability sampling techniques rely on the researcher's judgment. Consequently, they do not permit an objective evaluation of the precision of the sample results, and the estimates obtained are not statistically projectable to the population.
The commonly used the non-probability sampling techniques include:
- convenience sampling
- judgmental sampling
- quota sampling
- snowball sampling
Probability sampling techniques include:
- simple random sampling
- systematic sampling
- stratified sampling
- cluster sampling
- sequential sampling
- double sampling
When conducting international marketing research, it is desirable to achieve comparability and sample composition and representativeness even though this may require the use of different sampling techniques in different countries. It is unethical and misleading to treat nonprobability samples as probability samples and project the results to the target population. The Internet and computers can be used to make the sampling design process more effective and efficient.
Population -- the aggregate of all the elements, sharing some common set of characteristics, that comprises the universe for the purpose of the marketing research problem
Census -- a complete enumeration of the elements of the population or study objects
sample -- a subgroup of the elements of the population selected for participation in the study
target population -- the collection of elements or objects that possesses the information sought by the researcher and about which inferences are to be made
element -- objects that possess the information sought by the researcher and about which inferences are to be made
sampling unit -- the basic unit containing the elements of the population to be sampled
sampling frame -- a representation of the elements of the target population. It consists of a list or set of directions for identifying the target population
Bayesian approach -- a selection method with the elements are selected sequentially. This approach explicitly incorporates prior information about population parameters as well as costs and probabilities associated with making wrong decisions
sampling with replacement -- a sampling technique in which an element can be included in the sample more than once
sampling without replacement -- a sampling technique in which an element cannot be included in the sample more than once
sample size -- the number of elements to be included in the study
nonprobability sampling -- sampling techniques that do not use chance selection procedures. Rather, they rely on a personal judgment of the researcher
probability sampling -- a sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample
convenience sampling -- a nonprobability sampling technique that attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer
judgmental sampling -- a form of convenience sampling in which the population elements are purposively based on the judgment of the researcher
quota sampling -- and nonprobability sampling technique that is a two-stage restricted judgmental sampling. The first stage consists of developing control categories or quotas of population elements. In the second stage, sample elements are selected based on convenience or judgment
Snowball sampling -- and nonprobability sampling technique in which an initial group of respondents is selected randomly. Subsequent respondents are selected based on the referrals or information provided by the initial respondents. This process may be carried out in ways by obtaining referrals from referrals
simple random sampling (SRS) -- a probability sampling technique in which each element in the population has a known and equal probability of selection. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame
systematic sampling -- a probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame
stratified sampling -- a probability sampling technique that uses a two step process to partition the population into subpopulations, or strata. Elements are selected from each stratum by a random procedure
cluster sampling -- first, the target population is divided into mutually exclusive and collectively exhaustive subpopulations called clusters. Then, a random sample of clusters is selected based on a probability sampling technique such as simple random sampling. For each selected cluster, either all the elements are included in the sample or a sample of elements is drawn probabilistically
area sampling -- a common form of cluster sampling in which the clusters consist of geographic areas such as countries, housing tracts, block, or other area descriptions
probability proportionate to size sampling -- a selection method with the clusters are selected with probability proportional to size and a probability of selecting a sampling unit and a selected cluster varies inversely with the size of the cluster
sequential sampling -- a probability sampling technique in which the population elements are sampled sequentially, data collection and analysis are done at each stage, and a decision is made as to whether additional population elements should be sampled
double sampling -- a sampling technique in which certain population elements are sampled twice
questionnaire and form design
Chapter 10
To collect quantitative primary data, the researcher must design a questionnaire or an observation form. A questionnaire has three objectives.
Determining the wording of each question involves defining the issue, using ordinary words, using unambiguous words, and using dual statements. The researcher should avoid leading questions, implicit alternatives, implicit assumptions, and generalizations and estimates. Once the questions have been worded, the order in which they will appear in the questionnaire must be decided. Special consideration should be given to opening questions, type of information, difficult questions, and the effect on subsequent questions. The questions should be arranged in a logical order.
The stage is now set for determining the form and layout of the questions. Several factors are important in reproducing the questionnaire.
These include:
The questionnaire should be adapted to the specific cultural environment and should not be biased in terms of any one culture. Also, the questionnaire may have to be suitable for ministration by more than one method as different interviewing methods may be used in different countries. Several ethical issues related to the researcher respondent relationship and the researcher client relationship may have to be addressed. The Internet and computers can greatly assist the researcher in designing sound questionnaires and observational forms.
Questionnaire -- a structured technique for data collection that consists of a series of questions, written or verbal, that respondent answers
double-barreled question -- a single question that attempts to cover two issues. Such questions can be confusing to respondents and result in ambiguous responses
filter questions -- an initial question in a questionnaire that screens potential respondents to ensure they meet the requirements of the sample
telescoping -- a psychological phenomenon that takes place when an individual telescopes or compress time by remembering an event as occurring more recently than actually occurred
unstructured questions -- open-ended questions that respondent answer in their own words
structured questions -- questions that pre-specify the set of response alternatives and the response format. A structured question could be multiple choice, dichotomous, or a scale
order or position bias -- a respondents tendency to check an alternative merely because it occupies a certain position or is listed in a certain order
dichotomous question -- a structured question was only two response alternatives, such as yes and no
leading question -- a question that gives a respondent a clue as to what answer is desired or leave the responded to answer in a certain way
implicit alternative -- an alternative that is not explicitly expressed
classification information -- socioeconomic and demographic characteristics used to classify respondents
identification information -- a type of information obtained in a questionnaire that includes name, address, and phone number
funnel approach -- a strategy for ordering questions in a questionnaire in which the sequence starts with the general questions that are followed by progressively specific questions, in order to prevent specific questions from biasing general questions
branching questions -- question used to guide an interviewer through a survey of directing the interviewer to different spots on the questionnaire depending on the answer given
pre-coding -- and questionnaire design, assigning a code to every conceivable response before data collection
pretesting -- the testing of the questionnaire on a small sample of respondents for the purpose of improving the questionnaire by identifying and eliminating potential problems
To collect quantitative primary data, the researcher must design a questionnaire or an observation form. A questionnaire has three objectives.
- It must translate the information needed into a set of specific questions the respondents can and will answer.
- It must motivate respondents to complete the interview.
- It must minimize response error.
- the information needed
- the type of interview method
- decide on the content of individual questions
- questions must overcome the respondents inability and unwillingness to answer
- decide on question structure
- determine the wording of each question
- ordering of the questions
- determine form and layout of the questions
- determined reproduction methods
- pre-test
Determining the wording of each question involves defining the issue, using ordinary words, using unambiguous words, and using dual statements. The researcher should avoid leading questions, implicit alternatives, implicit assumptions, and generalizations and estimates. Once the questions have been worded, the order in which they will appear in the questionnaire must be decided. Special consideration should be given to opening questions, type of information, difficult questions, and the effect on subsequent questions. The questions should be arranged in a logical order.
The stage is now set for determining the form and layout of the questions. Several factors are important in reproducing the questionnaire.
These include:
- appearance
- use of booklets
- fitting an entire question on a page
- response category format
- avoiding overcrowding
- placement of directions
- color coding
- easy to read format
- cost
- extent of pretesting
- nature of respondents
- type of interviewing method
- type of interviewers
- sample size
- protocol analysis and debriefing
- editing and analysis
The questionnaire should be adapted to the specific cultural environment and should not be biased in terms of any one culture. Also, the questionnaire may have to be suitable for ministration by more than one method as different interviewing methods may be used in different countries. Several ethical issues related to the researcher respondent relationship and the researcher client relationship may have to be addressed. The Internet and computers can greatly assist the researcher in designing sound questionnaires and observational forms.
Questionnaire -- a structured technique for data collection that consists of a series of questions, written or verbal, that respondent answers
double-barreled question -- a single question that attempts to cover two issues. Such questions can be confusing to respondents and result in ambiguous responses
filter questions -- an initial question in a questionnaire that screens potential respondents to ensure they meet the requirements of the sample
telescoping -- a psychological phenomenon that takes place when an individual telescopes or compress time by remembering an event as occurring more recently than actually occurred
unstructured questions -- open-ended questions that respondent answer in their own words
structured questions -- questions that pre-specify the set of response alternatives and the response format. A structured question could be multiple choice, dichotomous, or a scale
order or position bias -- a respondents tendency to check an alternative merely because it occupies a certain position or is listed in a certain order
dichotomous question -- a structured question was only two response alternatives, such as yes and no
leading question -- a question that gives a respondent a clue as to what answer is desired or leave the responded to answer in a certain way
implicit alternative -- an alternative that is not explicitly expressed
classification information -- socioeconomic and demographic characteristics used to classify respondents
identification information -- a type of information obtained in a questionnaire that includes name, address, and phone number
funnel approach -- a strategy for ordering questions in a questionnaire in which the sequence starts with the general questions that are followed by progressively specific questions, in order to prevent specific questions from biasing general questions
branching questions -- question used to guide an interviewer through a survey of directing the interviewer to different spots on the questionnaire depending on the answer given
pre-coding -- and questionnaire design, assigning a code to every conceivable response before data collection
pretesting -- the testing of the questionnaire on a small sample of respondents for the purpose of improving the questionnaire by identifying and eliminating potential problems
Saturday, May 19, 2007
measurement and scaling -- noncomparative scaling techniques
chapter 9
In noncomparative scaling, each object is scaled independently of the other objects in the stimulus set. the resulting data is generally assumed to be interval or ratio scale. noncomparative rating scales can be either continuous or itemized. The itemized rating scales are further classified as Likert, semantic differential, or Stapel scales. When using noncomparative itemized rating scales, the researcher must decide on the number of scale categories, balanced verses unbalanced scales, odd or even number of categories, forced versus non-forced scales, nature and degree of verbal description, and a physical form or configuration.
Multi-item scales consist of a number of rating scale items. The scale should be evaluated in terms of reliability and validity. Reliability refers to the extent to which a scale produces consistent results if repeated measurements are made. Approaches to assessing reliability include test -- retest, alternative forms, and internal consistency. Validity, or accuracy of measurement, may be assessed by evaluating content validity, criterion validity, and construct validity.
The choice of particular scaling techniques in a given situation should be based on theoretical and practical considerations. As a general rule, the scaling technique used should be the one that will yield the highest level of information feasible. Also, multiple measures should be obtained.
An international marketing research, special attention should be debated to determining equivalent verbal descriptors in different languages and cultures. The researcher has a responsibility to both the client and the respondents to insure the applicability and usefulness of the scales. The Internet and computers are useful for developing and testing continuous and itemized rating scales, particularly multi-item scales.
Non-comparative scale -- 1 of two types of scaling techniques in which each stimulus object is scaled independently of the other objects in the stimulus set
continuous rating scale -- also referred to as graphic rating scale, this measurement scale has the respondents rate the objects by placing a market the appropriate position on a line that runs from one extreme of the criterion variable to the other
itemized rating scale -- a measurement scale having numbers and/or brief descriptions associated with each category. The categories are ordered in terms of scale position
Likert scale -- a measurement scale of five response categories ranging from strongly disagree to strongly agree, which requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements related to the stimulus objects
semantic differential -- a seven-point rating scale with endpoint associated with bipolar labels that have semantic meaning
Stapel scale -- a scale for measuring attitudes that consists of a single objective in the middle of an even numbered range of values, from negative five to positive five, without a neutral point (zero)
balanced scale -- a scale with an equal number of favorable and unfavorable categories
forced rating scales -- a reading scale that forces the respondents to express an opinion because "no opinion" or "no knowledge" option is not provided
measurement error -- the variation in the information sought by the researcher and the information generated by the measurement process employed
true score model -- a mathematical model that provides a framework for understanding the accuracy of measurement
systematic error -- systematic error affects the measurement in a constant way and represents stable factors that affect the observed score in some way each time the measurement is made
random error -- measurement error that arises from random changes or differences and respondents were measurement situations
reliability -- the extent to which a scale produces constant results if repeated measurements are made on the characteristic
test-retest reliability -- an approach for assessing reliability in which respondents are administered identical sets of scale items at two different times under a nearly equivalent conditions as possible
alternative forms reliability -- an approach for assessing reliability that requires to equivalent forms of the scale to be constructed and then the same respondents are measured at two different times
internal consistency reliability -- an approach for assessing the internal consistency of the set of items when several items are summated in order to form a total score for the scale
split- half reliability -- a form of internal consistency reliability and which the items constituting the scale are divided into two halves and the resulting half scores are correlated
coefficient alpha -- it's a measure of internal consistency reliability that is the average of all possible split half coefficients resulting from different splitting of the scale items
validity -- the extent to which differences and observe scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random errors
content validity -- a type of validity, sometimes called face validity, that consists of a subjective but systematic evaluation of the representativeness of the content of a scale for the measuring task at hand
criterion validity -- a type of validity that examines whether the measurement scale performs as expected in relation to other variables selected as meaningful criteria
construct validity -- a type of validity that addresses the question of what construct or characteristic the scale is measuring. An attempt is made to answer theoretical questions of why a scale works and what deductions can be made concerning the theory underlying the scale
convergent validity -- a measure of construct validity that measures the extent to which the scale correlates positively with other measures are the same construct
discriminant validity -- a type of construct validity that accesses the extent to which a measure does not correlate with other constructs from which it is supposed to differ
nomological validity -- a type of validity that assesses the relationship between theoretical constructs. It seeks to confirm significant correlations between constructs as predicted by theory
generalizability -- the degree to which a study based on a sample of applies to a universe of generalization
In noncomparative scaling, each object is scaled independently of the other objects in the stimulus set. the resulting data is generally assumed to be interval or ratio scale. noncomparative rating scales can be either continuous or itemized. The itemized rating scales are further classified as Likert, semantic differential, or Stapel scales. When using noncomparative itemized rating scales, the researcher must decide on the number of scale categories, balanced verses unbalanced scales, odd or even number of categories, forced versus non-forced scales, nature and degree of verbal description, and a physical form or configuration.
Multi-item scales consist of a number of rating scale items. The scale should be evaluated in terms of reliability and validity. Reliability refers to the extent to which a scale produces consistent results if repeated measurements are made. Approaches to assessing reliability include test -- retest, alternative forms, and internal consistency. Validity, or accuracy of measurement, may be assessed by evaluating content validity, criterion validity, and construct validity.
The choice of particular scaling techniques in a given situation should be based on theoretical and practical considerations. As a general rule, the scaling technique used should be the one that will yield the highest level of information feasible. Also, multiple measures should be obtained.
An international marketing research, special attention should be debated to determining equivalent verbal descriptors in different languages and cultures. The researcher has a responsibility to both the client and the respondents to insure the applicability and usefulness of the scales. The Internet and computers are useful for developing and testing continuous and itemized rating scales, particularly multi-item scales.
Non-comparative scale -- 1 of two types of scaling techniques in which each stimulus object is scaled independently of the other objects in the stimulus set
continuous rating scale -- also referred to as graphic rating scale, this measurement scale has the respondents rate the objects by placing a market the appropriate position on a line that runs from one extreme of the criterion variable to the other
itemized rating scale -- a measurement scale having numbers and/or brief descriptions associated with each category. The categories are ordered in terms of scale position
Likert scale -- a measurement scale of five response categories ranging from strongly disagree to strongly agree, which requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements related to the stimulus objects
semantic differential -- a seven-point rating scale with endpoint associated with bipolar labels that have semantic meaning
Stapel scale -- a scale for measuring attitudes that consists of a single objective in the middle of an even numbered range of values, from negative five to positive five, without a neutral point (zero)
balanced scale -- a scale with an equal number of favorable and unfavorable categories
forced rating scales -- a reading scale that forces the respondents to express an opinion because "no opinion" or "no knowledge" option is not provided
measurement error -- the variation in the information sought by the researcher and the information generated by the measurement process employed
true score model -- a mathematical model that provides a framework for understanding the accuracy of measurement
systematic error -- systematic error affects the measurement in a constant way and represents stable factors that affect the observed score in some way each time the measurement is made
random error -- measurement error that arises from random changes or differences and respondents were measurement situations
reliability -- the extent to which a scale produces constant results if repeated measurements are made on the characteristic
test-retest reliability -- an approach for assessing reliability in which respondents are administered identical sets of scale items at two different times under a nearly equivalent conditions as possible
alternative forms reliability -- an approach for assessing reliability that requires to equivalent forms of the scale to be constructed and then the same respondents are measured at two different times
internal consistency reliability -- an approach for assessing the internal consistency of the set of items when several items are summated in order to form a total score for the scale
split- half reliability -- a form of internal consistency reliability and which the items constituting the scale are divided into two halves and the resulting half scores are correlated
coefficient alpha -- it's a measure of internal consistency reliability that is the average of all possible split half coefficients resulting from different splitting of the scale items
validity -- the extent to which differences and observe scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random errors
content validity -- a type of validity, sometimes called face validity, that consists of a subjective but systematic evaluation of the representativeness of the content of a scale for the measuring task at hand
criterion validity -- a type of validity that examines whether the measurement scale performs as expected in relation to other variables selected as meaningful criteria
construct validity -- a type of validity that addresses the question of what construct or characteristic the scale is measuring. An attempt is made to answer theoretical questions of why a scale works and what deductions can be made concerning the theory underlying the scale
convergent validity -- a measure of construct validity that measures the extent to which the scale correlates positively with other measures are the same construct
discriminant validity -- a type of construct validity that accesses the extent to which a measure does not correlate with other constructs from which it is supposed to differ
nomological validity -- a type of validity that assesses the relationship between theoretical constructs. It seeks to confirm significant correlations between constructs as predicted by theory
generalizability -- the degree to which a study based on a sample of applies to a universe of generalization
measurement and scaling
Chapter 8
Measurement is the assignment of numbers or other symbols to characteristics of objects according to set rules. Scaling and involves the generation of a continuum upon which measured objects are located.
The four primary skills of measurement are:
Scaling techniques can be classified as comparative or non-comparative. Comparative scaling and involves a direct comparison of stimulus objects.
Comparative scales include:
Measurement -- the assignment of numbers or other symbols to characteristics of objects according to certain pre-specified rules
scaling -- the generation of a continuum upon which measured objects are located
nominal scale -- a scale whose numbers serve only as labels or tags for identifying and classifying objects with a strict one-to-one correspondence between the numbers in the objects
ordinal scale -- a ranking scale in which numbers are assigned to objects to indicate the relative extent to which some characteristic is possessed. Thus it is possible to determine whether an object has more or less of a characteristic and some other object
interval scale -- a scale in which the numbers are used to great objects such numerically equal distances on the scale represent equal distances in the characteristic being measured
ratio scale -- the highest scale. It allows the researcher to identify or classify objects, rank order the objects, and compare intervals or differences. It is also meaningful to compute ratios of scale values
comparative scales -- 1 or two types of scaling techniques in which there is direct comparison of stimulus objects with one another
non-comparative scales -- 1 of two types of scaling techniques in which each stimulus object is scaled independently of the other objects in the stimulus that
paired comparisons scaling -- a comparative scaling technique in which he respond it is presented with two objects at a time and asked to select one object in the pair according to some criterion. the data obtained are ordinal in nature
transitivity of preference -- an assumption made in order to convert paired comparison data to rank order data
rank order scaling -- a comparative scaling technique in which respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion
constant sum scaling -- a comparative scaling technique in which respondents are required to allocate a constant sum of the units such as points, dollars, stickers, or chips among a set of stimulus objects with respect to some criterion
Q-sort scaling -- a comparative scaling technique that uses a rank order procedure to sort objects based on similarity with respect to some criterion
Measurement is the assignment of numbers or other symbols to characteristics of objects according to set rules. Scaling and involves the generation of a continuum upon which measured objects are located.
The four primary skills of measurement are:
- nominal
- ordinal
- interval
- ratio
Scaling techniques can be classified as comparative or non-comparative. Comparative scaling and involves a direct comparison of stimulus objects.
Comparative scales include:
- paired comparisons
- rank order
- constant sum
- Q-sort properties
Measurement -- the assignment of numbers or other symbols to characteristics of objects according to certain pre-specified rules
scaling -- the generation of a continuum upon which measured objects are located
nominal scale -- a scale whose numbers serve only as labels or tags for identifying and classifying objects with a strict one-to-one correspondence between the numbers in the objects
ordinal scale -- a ranking scale in which numbers are assigned to objects to indicate the relative extent to which some characteristic is possessed. Thus it is possible to determine whether an object has more or less of a characteristic and some other object
interval scale -- a scale in which the numbers are used to great objects such numerically equal distances on the scale represent equal distances in the characteristic being measured
ratio scale -- the highest scale. It allows the researcher to identify or classify objects, rank order the objects, and compare intervals or differences. It is also meaningful to compute ratios of scale values
comparative scales -- 1 or two types of scaling techniques in which there is direct comparison of stimulus objects with one another
non-comparative scales -- 1 of two types of scaling techniques in which each stimulus object is scaled independently of the other objects in the stimulus that
paired comparisons scaling -- a comparative scaling technique in which he respond it is presented with two objects at a time and asked to select one object in the pair according to some criterion. the data obtained are ordinal in nature
transitivity of preference -- an assumption made in order to convert paired comparison data to rank order data
rank order scaling -- a comparative scaling technique in which respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion
constant sum scaling -- a comparative scaling technique in which respondents are required to allocate a constant sum of the units such as points, dollars, stickers, or chips among a set of stimulus objects with respect to some criterion
Q-sort scaling -- a comparative scaling technique that uses a rank order procedure to sort objects based on similarity with respect to some criterion
experimentation
Chapter 7
The scientific notion of causality implies that we can never prove that X causes Y. At best, we can only infer that X is one of the causes of Y in that it makes the occurrence of Y probable.
Three conditions must be satisfied before casual inferences can be made:
In designing an experiment, it is important to consider internal and external validity. Internal validity refers to whether the manipulation of the independent variables actually cause the effects of the dependent variables. External validity refers to the realizability of experimental results.
For the experiment to be valid, the researcher must control the threats imposed by extraneous variables, such as:
Although experiments have limitations in terms of time, cost, and administration, they are becoming increasingly popular in marketing. Test marketing is an important application of experimental design.
The internal and external validity of field experiments conducted overseas is generally lower in the United States. The level of development in many countries is lower, and the researcher lacks control over many of the marketing variables. The ethical issues involved in conducting casual research include describing the purpose of the experiment. Debriefing can be used to address some of these issues. The Internet and computers are very useful in the design and implementation of experiments.
Causality -- when the occurrence of X increases the probability of the occurrence of Y
concomitant variation -- a condition for inferring causality that requires the extent to which a cause,X, and an effect,Y, occurred together or buried together is predicted by the up office this under consideration
independent variables -- variables that are manipulated by the researcher and whose effects are measured and compared
test units -- individuals, organizations, or other entities whose response to independent variables or treatments is being studied
dependent variables -- variables that measure the effect of the independent variables on the test units
extraneous variables -- variables, other than the independent variables, that influence the response of the test units
experiment -- the process of manipulating one or more independent variables and measuring their effect on one or more dependent variables, while controlling for the extraneous variables
experimental design -- a set of experimental procedures specifying:
external validity -- a determination of whether the cause and effect relationships found in the experiment can be generalized
history (H) -- specific events that are external to the experiment but occur at the same time as the experiment
maturation (MA) -- an extraneous variable attributable to changes in the test units themselves that occur with the passage of time
Main testing effect (MT) -- an effect of testing occurring when a prior observation affects a later observation
interactive testing effect (IT) -- an effect in which a prior measurement affects the testing its response to the independent variable
instrumentation (I) -- an extraneous variable involving changes in the measurable instrument or in the observers were scores themselves
statistical regression (SR) -- an extraneous variable that occurs when testing its with extreme scores move closer to the average score during the course of the experiment
selection bias (SB) -- an extraneous variable attributable to the improper assignment of test units to treatment conditions
mortality (MO) -- an extraneous variable attributable to the loss of test units while the experiment is in progress
confounding variables -- synonymous with extraneous variables, used to illustrate that extraneous variables can confound the results by influencing the dependent variable
randomization -- 1 method of controlling extraneous variables that involves randomly assigning test units to experimental groups by using random numbers. Treatment conditions are also randomly assigned to experimental groups
matching -- 1 method of controlling extraneous variables that involves matching test units on a set of key background variables before assigning them to the treatment conditions
statistical control -- 1 method of controlling extraneous variables by measuring the extraneous variables and adjusting for their effects through statistical methods
design control -- 1 method of controlling extraneous variables that involves using specific experimental designs
pre-experimental designs -- designs that do not control for extraneous factors by randomization
true experimental designs -- experimental designs distinguished by the fact that the researcher can randomly assigned test units to experimental groups and also randomly assigned treatments to experimental groups
quasi-experimental designs -- designs that apply part of the procedures of true experimentation but lack full experimental control
statistical design -- designs that allow for the statistical control and analysis of external variables
one-shot case study -- a pre-experimental design in which a single group of test units is exposed to treatment X, and then a single measurement on the dependent variable is taken
one group pre-test - posttest design -- a pre-experimental design in which a group of test units is measured twice
static group -- a pre-experimental design in which there are two groups: the experimental group (EG), which is exposed to the treatment, and the control group (CG). Measurements on both groups are made only after the treatment, and test units are not assigned at random
pre-test - posttest control group design -- a true experimental design in which the experimental group is exposed to the treatment at the control group is not. Pre-test and posttest measurements are taken on both groups
posttest only control group design -- a true experimental design in which the experimental group is exposed to the treatment but the control group is not and no pretest measure is taken
Solomon four-group design -- a true experimental design that explicitly controls for interactive testing effects, in addition to controlling for all the other extraneous variables
time series design -- a quasi-experimental design that involves periodic measurements on the dependent variable for a group of test units. Then, the treatment is administered by the researcher or occurs naturally. After the treatment, periodic measurements are continued in order to determine the treatment effect
multiple time series design -- a time series design that includes another group of test units to serve as a control group
randomized block design -- a statistical design in which to test units are blocked on the basis of on external variable to ensure that the various experimental and control groups are matched closely on that variable
Latin square design -- a statistical design allows for the statistical control of two non-interacting external variables in addition to the manipulation of the independent variable
factorial design -- a statistical experimental design that is used to measure the effects of two or more independent variables at various levels and to allow for interactions between variables
laboratory environment -- an artificial setting for experimentation in which the researcher conducts the desired conditions
field environment -- an experimental location set in the actual market conditions
demand artifacts -- the respondents attempt to guess the purpose of the experiment and respond accordingly
test marketing -- an application of a controlled experiment done in limited, but carefully selected, test markets. It involves a replication of the planned national marketing program for a product in the test markets
test markets -- a carefully selected part of the marketplace that is particularly suitable for testmarketing
standard test market -- a test market in which the product is sold through regular distribution channels. For example, no special considerations are given to product simply because they are being testmarketed
controlled test market -- a testmarketing program conducted by an outside research company in field experimentation. The research company guarantees distribution of the product and retail outlet that represent a predetermined percentage of the market
simulated test market -- a quasi-test market in which respondents are preselected, then interviewed and observed on their purchases and attitudes toward the product
debriefing -- after the experiment, informing test subjects with the experiment was about and how they experimental manipulations were performed
The scientific notion of causality implies that we can never prove that X causes Y. At best, we can only infer that X is one of the causes of Y in that it makes the occurrence of Y probable.
Three conditions must be satisfied before casual inferences can be made:
- concomitant variation, which implies that X and Y must vary together in a hypothesized way
- timed order of occurrence of variables, which implies that X must precede Y
- a lemon nation of other possible casual factors, which implies that competing explanations must be ruled out
In designing an experiment, it is important to consider internal and external validity. Internal validity refers to whether the manipulation of the independent variables actually cause the effects of the dependent variables. External validity refers to the realizability of experimental results.
For the experiment to be valid, the researcher must control the threats imposed by extraneous variables, such as:
- history
- maturation
- testing (main and interactive testing effects)
- instrumentation
- statistical regression
- selection bias
- mortality
- randomization
- matching
- statistical control
- design control
- pre-experimental
- true experimental
- quasi-experimental
- statistical
Although experiments have limitations in terms of time, cost, and administration, they are becoming increasingly popular in marketing. Test marketing is an important application of experimental design.
The internal and external validity of field experiments conducted overseas is generally lower in the United States. The level of development in many countries is lower, and the researcher lacks control over many of the marketing variables. The ethical issues involved in conducting casual research include describing the purpose of the experiment. Debriefing can be used to address some of these issues. The Internet and computers are very useful in the design and implementation of experiments.
Causality -- when the occurrence of X increases the probability of the occurrence of Y
concomitant variation -- a condition for inferring causality that requires the extent to which a cause,X, and an effect,Y, occurred together or buried together is predicted by the up office this under consideration
independent variables -- variables that are manipulated by the researcher and whose effects are measured and compared
test units -- individuals, organizations, or other entities whose response to independent variables or treatments is being studied
dependent variables -- variables that measure the effect of the independent variables on the test units
extraneous variables -- variables, other than the independent variables, that influence the response of the test units
experiment -- the process of manipulating one or more independent variables and measuring their effect on one or more dependent variables, while controlling for the extraneous variables
experimental design -- a set of experimental procedures specifying:
- the test units and sampling procedures
- independent variables
- dependent variables
- how to control the extraneous variables
external validity -- a determination of whether the cause and effect relationships found in the experiment can be generalized
history (H) -- specific events that are external to the experiment but occur at the same time as the experiment
maturation (MA) -- an extraneous variable attributable to changes in the test units themselves that occur with the passage of time
Main testing effect (MT) -- an effect of testing occurring when a prior observation affects a later observation
interactive testing effect (IT) -- an effect in which a prior measurement affects the testing its response to the independent variable
instrumentation (I) -- an extraneous variable involving changes in the measurable instrument or in the observers were scores themselves
statistical regression (SR) -- an extraneous variable that occurs when testing its with extreme scores move closer to the average score during the course of the experiment
selection bias (SB) -- an extraneous variable attributable to the improper assignment of test units to treatment conditions
mortality (MO) -- an extraneous variable attributable to the loss of test units while the experiment is in progress
confounding variables -- synonymous with extraneous variables, used to illustrate that extraneous variables can confound the results by influencing the dependent variable
randomization -- 1 method of controlling extraneous variables that involves randomly assigning test units to experimental groups by using random numbers. Treatment conditions are also randomly assigned to experimental groups
matching -- 1 method of controlling extraneous variables that involves matching test units on a set of key background variables before assigning them to the treatment conditions
statistical control -- 1 method of controlling extraneous variables by measuring the extraneous variables and adjusting for their effects through statistical methods
design control -- 1 method of controlling extraneous variables that involves using specific experimental designs
pre-experimental designs -- designs that do not control for extraneous factors by randomization
true experimental designs -- experimental designs distinguished by the fact that the researcher can randomly assigned test units to experimental groups and also randomly assigned treatments to experimental groups
quasi-experimental designs -- designs that apply part of the procedures of true experimentation but lack full experimental control
statistical design -- designs that allow for the statistical control and analysis of external variables
one-shot case study -- a pre-experimental design in which a single group of test units is exposed to treatment X, and then a single measurement on the dependent variable is taken
one group pre-test - posttest design -- a pre-experimental design in which a group of test units is measured twice
static group -- a pre-experimental design in which there are two groups: the experimental group (EG), which is exposed to the treatment, and the control group (CG). Measurements on both groups are made only after the treatment, and test units are not assigned at random
pre-test - posttest control group design -- a true experimental design in which the experimental group is exposed to the treatment at the control group is not. Pre-test and posttest measurements are taken on both groups
posttest only control group design -- a true experimental design in which the experimental group is exposed to the treatment but the control group is not and no pretest measure is taken
Solomon four-group design -- a true experimental design that explicitly controls for interactive testing effects, in addition to controlling for all the other extraneous variables
time series design -- a quasi-experimental design that involves periodic measurements on the dependent variable for a group of test units. Then, the treatment is administered by the researcher or occurs naturally. After the treatment, periodic measurements are continued in order to determine the treatment effect
multiple time series design -- a time series design that includes another group of test units to serve as a control group
randomized block design -- a statistical design in which to test units are blocked on the basis of on external variable to ensure that the various experimental and control groups are matched closely on that variable
Latin square design -- a statistical design allows for the statistical control of two non-interacting external variables in addition to the manipulation of the independent variable
factorial design -- a statistical experimental design that is used to measure the effects of two or more independent variables at various levels and to allow for interactions between variables
laboratory environment -- an artificial setting for experimentation in which the researcher conducts the desired conditions
field environment -- an experimental location set in the actual market conditions
demand artifacts -- the respondents attempt to guess the purpose of the experiment and respond accordingly
test marketing -- an application of a controlled experiment done in limited, but carefully selected, test markets. It involves a replication of the planned national marketing program for a product in the test markets
test markets -- a carefully selected part of the marketplace that is particularly suitable for testmarketing
standard test market -- a test market in which the product is sold through regular distribution channels. For example, no special considerations are given to product simply because they are being testmarketed
controlled test market -- a testmarketing program conducted by an outside research company in field experimentation. The research company guarantees distribution of the product and retail outlet that represent a predetermined percentage of the market
simulated test market -- a quasi-test market in which respondents are preselected, then interviewed and observed on their purchases and attitudes toward the product
debriefing -- after the experiment, informing test subjects with the experiment was about and how they experimental manipulations were performed
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