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Linear network analysis is a tool for determining the reaction of networks to changes in the inputs. While most commonly applied to electrical power, data and communication networks, linear network analysis is useful for examining any interconnected entity that has inputs and outputs. When behavior is not linear, networks react in an almost linear fashion over particular time frames. Linear network analysis can provide good approximations over these time frames, and can analyze almost any network.
Confirmatory factor analysis, or CFA, is a statistical method that verifies the factor structure of a set of observed variables. For example, a business firm is analyzing the factors that influence online shopping, and theory and earlier research suggest cost, convenience and product choices as the three factors that most influence online shopping. The business performs CFA to confirm whether these three factors are, in fact, the most influential factors for online shopping. CFA has three estimation methods: factor pattern, factor correlation and factor uniqueness.
Vector autoregression analysis (or VAR analysis) is a statistically valid way of evaluating the co-development of multiple sets of time series. A time series is a set of statistical data that shows how a specific phenomenon changes quantitatively with respect to time. When there are multiple sets of time series, statisticians may use the vector autoregression analysis as a theory-less way of investigating relationships between the different time series. Recently, some statisticians have begun to introduce dynamic factor models (models that use diffusion indices in the mathematical construction of vector autoregression models). This introduction has changed how autoregression analyses are…
Factor analysis aims to reduce observable data or data from tests into a theoretical framework. Principal axis factor analysis is a specific type of factor analysis. Originally developed in relation to the intelligence quotient, factor analysis now has applications in behavioral science and business.
"Stakeholder" refers to anyone with an interest in, influence over or influenced by an entity or project. Most commonly, stakeholder refers to people involved in or concerned with a corporation. Professors Charles Eesly and Michael Lenox state that secondary stakeholders are groups and people who "affect or are affected by firm behavior" but are not officially part of the corporation. Nongovernmental organizations, activists, churches and neighbors are common secondary stakeholders.
The U.S. Environmental Protection Agency (EPA) has approved several analytical methods directed at drinking water. Field and laboratory technicians use analytical methods by taking samples of chlorine to undergo immediate testing because chlorine is not stable in water and the samples will break down. Daily monitoring and measuring against previous water samples taken the same time of day classifies the results in ranges from free of chlorine to total chlorine residual.
Federal and state authorities regulate and monitor laboratory analysis of food, water and pollution control. Florida, to a large degree, enforces the use of mandatory federal standards for analytical methods as set out by the appropriate federal regulatory agencies---the U.S. Food and Drug Administration and the Environmental Protection Agency, for example. These analytical methods must meet state requirements and state-designated federal requirements.
Optical activity describes the behavior and reaction a person's eye has to specific stimuli. For example, if one were to look into a kaleidoscope as it is rotating, the eye would respond by adjusting to the amount of light, color and shapes portrayed in the device. These adjustments can be measured with scientific precision to help researchers determine the impact of observed rotation. According to the Ted Ankara College Chemistry Department in Turkey, results from similar testing using scientific equipment known as a polarimeter is used to determine optical rotation. Data is gathered from specific and segmented rotations and is…
Experimental work in any scientific field relies on measurements. Whether it is a geographic information system, a statistical test or an instrument in the laboratory, it is important for the analytical tool used in making these measurements to possess certain attributes. When researchers in any field use an analytical method, they need to prove that the method is capable of delivering results that are accurate as well as precise. Although this article uses the example of pharmaceutical testing, the concepts are equally relevant to all physical and social sciences.
The primary, secondary and tertiary sectors represent various business types and the goods they produce and sell. Each sector relies on the others to function properly and efficiently within the economy. Every job, in every industry, falls into one or more of these sector types.
No matter what the industry, sector, business or project, there are often more than one group or level of parties involved. In business and in government, these groups are often referred to as "stakeholders." Since outcomes and projects affect different groups differently, there are, usually, three levels of stakeholders defined: primary, secondary and tertiary.
Factor analysis and principal component analysis are techniques used to analyze variables for correlation. Specifically, both methods work by helping you identify which variables are correlated, if any, out of a set. For example, either method could be used to examine whether there is a relationship between socioeconomic status, political affiliation, and age, or some combination thereof. These methods are used frequently in business research. Both factor analysis and principal component analysis can produce similar results. However, these techniques are significantly different in several ways.
Businesses can benefit from studying and understanding the relationships between variables that affect the different aspects of their operations. Factor analysis is a method of statistical analysis that helps analysts discover those relationships. Their findings can apply to areas as diverse as financial decisions, public relations and the evaluation of potential and current employees.
Graphical models make use of the concept of modularity. They involve a combination of simpler parts to build a more complex system. Graphical models also provide a framework for the design of new systems.
Two or more groups may differ in several ways. For example, two groups of military officer candidates, one group promotable and one not, may differ in physical fitness, education, training and other factors. To determine which factors are the most important in distinguishing these two groups, use discriminant analysis.
Factor analysis describes variables through a statistical method. It takes a wide selection of data and consolidates it into cohesive statistics to make the information more tangible. Sometimes creating a way to present the information to the audience in a cohesive manner can be almost as challenging as understanding the large amount of information to synthesize. Use this hypothetical example of survey results to understand how to report factor analysis results.
The Five Forces Analysis, developed by Michael Porter, facilitates the marketer to compare competitive environments. As in the case of the PEST (Political, Economic, Social and Technological) analysis, it is a tool for environmental audit. The Five Forces Analysis, however, is geared toward an SBU (Strategic Business Unit), which is effectively a single business. The Five Forces Analysis is concerned with factor groups or areas that are relevant to stand-alone businesses.
Stakeholders are people or groups interested in a project, program or company. Stakeholders have varying interests depending on their relationship with the project, but knowing whether they have primary or secondary interests may be valuable when doing an analysis of the situation.
Factor analysis is a process by which numerous variables are identified for a particular subject, such as why consumers buy cell phones. Factor analysis, after compiling all of the variables that go into a consumer's choice, then attempts to identify certain "factors" that are critical to the purchase, with the resulting factors being used in the marketing of cell phones. This same analysis could be used in virtual any business.
Organizational consolidation has many potential benefits for an organization seeking to make better use of resources. A team examines all resources belonging to an organization, including assets, locations, equipment, personnel and organizational structures. After analysis of all factors, the team creates a more logical scheme to utilize resources. Those resources no longer needed under a new scheme are sold, closed or disposed of in a cost-effective manner.
The spinning of the Earth causes day to turn to night, while the rotation of the Earth around the sun once a year causes summer to become winter. Combined, these two movements of the planet dictate our daily weather and global climate by affecting wind direction, temperature, ocean currents and precipitation.
Business managers swamped by a glut of market research information may turn to factor analysis to help sort through the information. Factor analysis can help you extract meaning from interconnected data in research results, plus uncover associations with survey responses you wouldn’t notice from examining the surface data alone. By reducing the array of issues to analyze and monitor, you can make better decisions about your marketing programs and respond faster to market trends.
Companies facing an overwhelming amount of marketing research information, particularly survey data such as customer satisfaction results, attitudes and usage data, or employee feedback, can use factor analysis to reduce the data into much more manageable and meaningful intelligence. Factor analysis is foremost a data reduction technique: It takes the entire data set and whittles the individual variables down to underlying factors. Factor analysis can analyze "interdependence" by tying together variables highly correlated with one another to reveal patterns flowing just beneath the surface, allowing new insights into the data under study.
Factor analysis reduces large sets of data, such as survey data, to explain related outcomes in terms of a small number of underlying factors. Making the results of a factor analysis understandable to any audience, regardless of statistical knowledge, poses a challenge as great as the analysis itself. Follow the steps below to prepare a presentation on a hypothetical survey.
Evaluation research occurs in a variety of fields, including education, business, engineering, public policy and the physical and social sciences. Specialists in these disciplines use a range of analytical methods, both qualitative and quantitative, to conduct evaluations.
ING Groep N.V (NYSE: ING [ADR], Euronext Amsterdam: ING), a Netherlands-headquartered insurance and financial services conglomerate that is in the process of revamping its business strategies in response to changes in the global insurance and financial services industries.
A factor analysis is utilized to discover factors among observed variables or 'latent' variables. Similarly stated, if a data set contains an overwhelming number of variables, a factor analysis may be performed to reduce the number of variables for analysis. A factor analysis will group similar variables, producing a set of factors, or compiled variables, to use for further analysis. A statistical analysis software package will be instrumental in the Factor Analysis calculation. Examples of statistical analysis packages are SPSS and SAS.
Statistics are used in a variety of fields from sports to television viewing to business. Factor analysis is a statistic that is used to isolate variable's effects on one another. For marketing, it is used to understand which factors affect product purchasing. This type of information translates directly to a company's bottom line.
Simulation analysis is an effective way of projecting risk and probabilistic behavior in nearly any stochastic system. A good simulation analysis process can help you understand the behavior of elements and systems in business, politics and even large-scale social systems. Follow a few basic steps to conduct a simulation analysis properly.
