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ADVANCED AND MULTIVARIATE STATISTICAL METHODS PRACTICAL APPLICATION AND INTERPRETATION 7TH, Exams of Quantitative Techniques

Harvard UniversityQuantitative Techniques

ADVANCED AND MULTIVARIATE STATISTICAL METHODS PRACTICAL APPLICATION AND INTERPRETATION 7TH EDITION CRAIG MERTLER RACHEL VANNATTA KRISTINA TEST BANK COMPREHENSIVE TEST PAPER 2026 COMPLETE ANSWERS ACCURATE

Typology: Exams

2025/2026

Available from 02/19/2026

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ADVANCED AND MULTIVARIATE
STATISTICAL METHODS PRACTICAL
APPLICATION AND INTERPRETATION 7TH
EDITION CRAIG MERTLER RACHEL
VANNATTA KRISTINA TEST BANK
COMPREHENSIVE TEST PAPER 2026
COMPLETE ANSWERS ACCURATE
⫸ Characteristics of big data Answer: -Volume: magnitude of
information being collected
-Variety: many different types of data and variables
-Velocity: very fast data generation
-Veracity: trustworthiness and accuracy of data is more important due to
the amount of data
-Variability: variation of the flow of data
-Value: the notion that abundance, not scarcity, is the driver of value in
this new area
⫸ Challenges with the variety of data Answer: -How do you analyze all
those different variables
-How do you analyze the nonmetric aspects of the data
⫸ Impacts of big data analysis on organizational decisions Answer: -
Has become widely spread due to the potential benefit in organizational
decision-making
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Download ADVANCED AND MULTIVARIATE STATISTICAL METHODS PRACTICAL APPLICATION AND INTERPRETATION 7TH and more Exams Quantitative Techniques in PDF only on Docsity!

ADVANCED AND MULTIVARIATE

STATISTICAL METHODS PRACTICAL

APPLICATION AND INTERPRETATION 7TH

EDITION CRAIG MERTLER RACHEL

VANNATTA KRISTINA TEST BANK

COMPREHENSIVE TEST PAPER 2026

COMPLETE ANSWERS ACCURATE

⫸ Characteristics of big data Answer: - Volume: magnitude of information being collected

  • Variety: many different types of data and variables
  • Velocity: very fast data generation
  • Veracity: trustworthiness and accuracy of data is more important due to the amount of data
  • Variability: variation of the flow of data
  • Value: the notion that abundance, not scarcity, is the driver of value in this new area ⫸ Challenges with the variety of data Answer: - How do you analyze all those different variables
  • How do you analyze the nonmetric aspects of the data ⫸ Impacts of big data analysis on organizational decisions Answer: - Has become widely spread due to the potential benefit in organizational decision-making
  • Using Big Data enables managers to decide on the basis of evidence rather than intuition ⫸ Impacts of big data analysis on academic research Answer: - Is slower to adopt and integrate big data
  • As researchers become more aware of these opportunities in both data and techniques their utilization will naturally increase ⫸ Impact of big data on analytics and the analyst Answer: - Might require changes, such as changing the analytics process and the qualities required of the analyst, increasing specialization or maybe diversification, and what is the role of domain knowledge?
  • There is an increase in the amount of analytic sub-domains, such as text processing, image analysis, social media analysis and so on, which provide inputs into more generalized analysis to extend the scope of the factors considered
  • A number of skills are emerging that serve all analysts, like visualization and managing dimensionality ⫸ 5 types of analysis Answer: - Descriptive
  • Inquisitive
  • Predictive
  • Prescriptive
  • Pre-emptive

⫸ Why distinguish between models Answer: - Each will continue to evolve separately, but the strengths and weaknesses of each can enable researchers from both to understand the situations and research questions most suited to each approach

  • Some cases are too complex, thus requiring algorithmic models, whilst other cases search for understanding processes and thus should use statistical models ⫸ Characteristics of Statistical/Data Models Answer: - Research Objective: Primarily Explanation
  • Research Paradigm: Theory-based (deductive)
  • Nature of Problem: Structured
  • Nature of Model Development: Confirmatory
  • Type of Data Analyzed: Well defined, collected for purpose of the research
  • Scope of the Analysis: Small to large datasets (number of variables and/or observations) ⫸ Characteristics of Data mining/Algorithmic models Answer: - Research Objective: Prediction
  • Research Paradigm: Heuristic-based (inductive)
  • Nature of Problem: Unstructured
  • Nature of Model Development: Exploratory
  • Type of Data Analyzed: Undefined, generally analysis used data available
  • Scope of the Analysis: Very large datasets (number of variables and/or observations) ⫸ Causal inference Answer: - Movement beyond statistical inference to the stronger statement of "cause and effect" in non-experimental situations
  • Previously thought to be for randomized controlled experiments, but now researchers have the theoretical frameworks for understanding the requirements for causal inferences in non-experiments and have techniques applicable to data not gathered in experiments to draw causal inferences ⫸ How do you move from statistical inference to causal analysis Answer: - Requires a paradigm shift incorporating an emphasis on the assumptions that are the foundation of all causal inferences and a framework for formulating and then specifying these assumptions
  • The result is a general theory of causation based on the structural causal model, where the directed acyclic graph (DAG) (graphical representation of the causal relationships) ⫸ Multivariate analysis Answer: All statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation (basically any with more than 2 variables) ⫸ Variate Answer: - The building block of multivariate analysis, which is a linear combination of variables with empirically determined weights
  • The variables are specified by the researcher whereas the weights are determined by the multivariate technique to meet a specific objective
  • The numbers utilized in ordinal scales, however, are really non- quantitative because they indicate only relative positions in an ordered series, no measure of the actual amount or magnitude in absolute terms
  • For instance, satisfaction scale or level of degree ⫸ Metric measurement scales Answer: - Metric data are used when subjects differ in amount or degree on a particular attribute
  • Reflect relative quantity or degree and are appropriate for attributes involving amount or magnitude
  • The two different metric measurement scales are interval and ratio scales ⫸ Interval scales Answer: - Have constant units of measurement, so differences between any two adjacent points on any part of the scale are equal
  • Have an arbitrary zero point, like the 0 in Fahrenheit and Celsius not being the same ⫸ Ratio scales Answer: Only differ from interval scales in the zero point, which uses an absolute zero point such as weight ⫸ Why is it important to understand the different types of measurement scales Answer: - The researcher must identify the measurement scale of each variable used, so that nonmetric data are not incorrectly used as metric data, and vice versa
  • The measurement scale is also critical in determining which multivariate techniques are the most applicable to the data, with considerations made for both independent and dependent variables ⫸ Measurement error Answer: - The degree to which the observed values are not representative of the "true" values
  • All variables used in multivariate techniques must be assumed to have some degree of measurement error ⫸ Validity Answer: - The degree to which a measure accurately represents what it is supposed to
  • Starts with a thorough understanding of what is to be measured and then making the measurement as "correct" and accurate as possible, however accurate does not ensure validity as it could be accurate at measuring the wrong thing ⫸ Reliability Answer: - The degree to which the observed variable measures the "true" value and is "error free" (opposite of measurement error)
  • If the same measure is asked repeatedly, for example, more reliable measures will show greater consistency than less reliable measures ⫸ Multivariate measurements Answer: - Can help reduce measurement error by having several variables joined in a composite measure to represent a concept
  • The objective is to avoid the use of only a single variable to represent a concept and instead to use several variables as indicators, all

⫸ Specifying the Variate Variables Answer: The primary decision here is whether to use the individual variables or to perform some form of dimensional reduction, such as exploratory factor analysis ⫸ Using the original variables Answer: - May seem the obvious choice as it preserves the characteristics of the variables and may make the results more interpretable and credible

  • There are pitfalls: the effect of including many variables and then trying to interpret the impact of each variable and multicollinearity ⫸ Dimensional reduction Answer: - Finding combinations of the individual variables that captures the multicollinearity among a set of variables and allows for a single composite value representing the set of variables
  • When doing this, the researcher must recognize that now the variables in the analysis are composites and the impacts for a composite represent the shared effect of those variables, not the individual variables themselves
  • Dimensional reduction can be done under the control of the researcher or through software programs ⫸ Variable Selection Answer: - One decision is if the researcher wants to control the specific variables to be included in the analysis or let the software determine the "best" set of variables to constitute the variate
  • With simultaneous (all variables entered simultaneously) or confirmatory (only a set or sequential sets of variables tested), the researcher can control the exact variables in the model
  • With software control the software employs an algorithm to decide which variables are to be concluded, most commonly with the sequential approach (variables entered until no other impactful variables can be found) ⫸ Principles for deciding how to specify the variate variables and select the variable Answer: - Specification of the variate is critical: The more control the researcher retains on which variables are inputs to the model allows for more specificity in how the model answers the specific research question
  • Variable selection is necessary: Researchers should try a number of alternative models in specifying their research, perhaps formulating several alternative models that vary based on whether the researcher controls the process or the software
  • Researcher control is preferred: While options exist for software control, researcher control provides for the analysis to test many different model specifications and provides a better outcome ⫸ 2 factors distinguishing dependence models Answer: - Single equation versus multi-equation models
  • Among the single equation models, general linear model or generalized linear model ⫸ Single Versus Multiple Equation Answer: - The most common applications of multivariate models are the single equation forms, like multiple regression or ANOVA
  • Single equation provides an approach for specifying a single variate's relationship with an outcome variable

⫸ 3 factors impacting statistical power Answer: - Effect size

  • Significance level
  • Sample size ⫸ Effect size Answer: - The probability of achieving statistical significance is based not only on statistical considerations, but also on the actual size of the effect
  • To assess the power of any statistical test, the researcher must first understand the effect being examined
  • Effect sizes are defined in standardized terms for ease of comparison, for instance mean differences through standard deviations ⫸ Significance level Answer: - As the alpha becomes more restrictive, power decreases
  • Researchers should consider the impact of a particular alpha level on the power before selecting the alpha level ⫸ Sample size Answer: - At any given alpha level, increased sample sizes always produce greater power for the statistical test
  • As sample sizes increase, researchers must decide if the power is too high, which means that smaller and smaller effects will be found to be statistically significant
  • The researcher must always be aware that sample size can affect the statistical test either by making it insensitive or overly sensitive

⫸ Using Power with Multivariate Techniques Answer: - Researchers can use power analysis either in the study design or after data is collected

  • In designing research studies, the sample size and alpha level are selected to achieve the desired power
  • Power also is examined after analysis is completed to determine the actual power achieved so the results can be interpreted ⫸ 3 judgements the researcher must make about the research objective and nature of the data Answer: - Can the variables be divided into independent and dependent classifications based on some theory?
  • If they can, how many variables are treated as dependent in a single analysis?
  • How are the variables, both dependent and independent, measured? ⫸ 2 categories of dependence techniques Answer: - The number of variables
  • The type of measurement scale employed by the variables ⫸ Classifications of dependence techniques regarding the number of variables Answer: - Single dependent variable
  • Several dependent variables
  • Several dependent/independent relationships ⫸ Classifications of dependence techniques regarding the type of measurement scale Answer: - Metric
  • Nonmetric
  • Confirmatory factor analysis ⫸ Selecting multivariate techniques for interdependence techniques for analyzing cases/respondents Answer: Cluster analysis ⫸ Selecting multivariate techniques for interdependence techniques for analyzing objects Answer: - Multidimensional scaling for metric attributes
  • Multidimensional scaling or correspondence analysis for nonmetric attributes ⫸ Exploratory factor analysis (including both principal component analysis and common factor analysis) Answer: - Statistical approach that can be used to analyze interrelationships among a large number of variables and to explain these variables in terms of their common underlying dimensions (factors)
  • The objective is to find a way of condensing the information contained in a number of original variables into a smaller set of variates (factors) with a minimal loss of information
  • By providing an empirical estimate of the structure of the variables considered, exploratory factor analysis becomes an objective basis for creating summated scales ⫸ Cluster analysis Answer: - An analytical technique for developing meaningful subgroups of individuals or objects
  • The objective is to classify a sample of entities (individuals or objects) into a small number of mutually exclusive groups based on the similarities among the entities
  • The groups are not predefined, the technique is used to identify the groups ⫸ Steps in cluster analysis Answer: - The first is the measurement of some form of similarity or association among the entities to determine how many groups really exist in the sample
  • The second step is the actual clustering process, whereby entities are partitioned into groups (clusters)
  • The final step is to profile the persons or variables to determine their composition ⫸ Multiple regression Answer: - Is the appropriate method of analysis when the research problem involves a single metric dependent variable presumed to be related to two or more metric independent variables
  • The objective of multiple regression analysis is to predict the changes in the dependent variable in response to changes in the independent variables, most often achieved through the statistical rule of least squares ⫸ Multivariate analysis of variance and covariance Answer: - Multivariate analysis of variance (MANOVA) is a statistical technique that can be used to simultaneously explore the relationship between several categorical independent variables (usually referred to as treatments) and two or more metric dependent variables
  • Multivariate analysis of covariance (MANCOVA) can be used in conjunction with MANOVA to remove (after the experiment) the effect
  • It provides the appropriate and most efficient estimation technique for a series of separate multiple regression equations estimated simultaneously
  • Characterized by 2 basic components: the structural model, which relates independent to dependent variables, and the measurement model, which enables the researcher to use several variables for a single variable ⫸ Confirmatory factor analysis Answer: - The researcher can assess the contribution of each scale item as well as incorporate how well the scale measures the concept (reliability)
  • The scales are then integrated into the estimation of the relationships between dependent and independent variables in the structural model ⫸ Partial least squares structural equation modeling (PLS-SEM) Answer: - Based on an analysis of total variance and also includes both a measurement model and a structural model
  • Theory and prior knowledge or other guidelines enable the researcher to distinguish which independent variables predict each dependent variable
  • The initial step in applying this method examines the measurement model and is referred to as confirmatory composite analysis
  • After the measurement models are determined to be valid and reliable, the analyst examines the structural model ⫸ Canonical correlation analysis Answer: - The objective is to correlate simultaneously several metric dependent variables and several metric independent variables
  • The underlying principle is to develop a linear combination of each set of variables (both independent and dependent) in a manner that maximizes the correlation between the two sets
  • The procedure involves obtaining a set of weights for the dependent and independent variables that provides the maximum simple correlation between the set of dependent variables and the set of independent variables ⫸ Conjoint analysis Answer: - A dependence technique that brings new sophistication to the evaluation of objects, such as new products, services, or ideas
  • The most direct application is in new product or service development, allowing for the evaluation of complex products while maintaining a realistic decision context for the respondent
  • The market researcher is able to assess the importance of attributes as well as the levels of each attribute while consumers evaluate only a few product profiles, which are combinations of product levels ⫸ Perceptual mapping Answer: - The objective is to transform consumer judgments of similarity or preference (e.g., preference for stores or brands) into distances represented in multidimensional space
  • If objects A and B are judged by respondents as being the most similar compared with all other possible pairs of objects, perceptual mapping techniques will position objects A and B in such a way that the distance between them in multidimensional space is smaller than the distance between any other pairs of objects
  • The resulting perceptual maps show the relative positioning of all objects, but additional analyses are needed to describe or assess which attributes predict the position of each object