About the National Security Agency

About the National Security Agency The National Security Agency is a highly specialized and vital unit of the American intelligence community that works to create and break secret codes, a science known as cryptology. The National Security Agency, or NSA, reports to the U.S. Department of Defense. The work of the National Security Agency is done in secret and in the name of national security. The government did not even acknowledge the NSA existed for some time. The National Security Agencys nickname is No Such Agency. What the NSA Does The National Security Agency gathers intelligence by conducting surveillance on its adversaries through the collection of phone-call, email and Internet data. The intelligence agency has two primary missions: preventing foreign adversaries from stealing sensitive or classified national security information from the United States, and collecting, processing and disseminating information from foreign signals for counterintelligence purposes. History of the National Security Agency The National Security Agency was created on Nov. 4, 1952, by President Harry S. Truman. The intelligence agencys foundation has its genesis in the work U.S. forces conducted during World War II in breaking German and Japanese codes, which it describes as a crucial factor in the Allied success against German U-Boats in the North Atlantic and victory at the Battle of Midway in the Pacific. How the NSA is Difference From the FBI and CIA The Central Intelligence Agency deals mostly with gathering intelligence on Americas enemies and conducts covert operations overseas. The Federal Bureau of Investigation, on the other hand, operates within the U.S borders as a law-enforcement agency. The NSA is primarily a foreign intelligence agency, meaning that it is authorized to collect data to prevent threats from foreign countries. However, in 2013 it was revealed that the NSA and FBI had allegedly been collecting phone-call data from Verizon and other information from servers operated by none U.S. Internet companies including Microsoft, Yahoo, Google, Facebook, AOL, Skype, YouTube, and Apple. Leadership of the NSA The head of the National Security Agency/Central Security Service is appointed by the secretary of the Department of Defense and approved by the president. The NSA/CSS director must be a commissioned military officer who has earned at least three stars. The current director of the intelligence agency is U.S. Army Gen. Keith B. Alexander. The NSA and Civil Liberties The surveillance activities of the NSA and every other intelligence agency often raise questions about civil liberties, and whether Americans are being subjected to unconstitutional invasions of privacy. In a statement published on the NSAs website, agency deputy director John C. Inglis wrote: Im often asked the question, Whats more important – civil liberties or national security? Its a false question; its a false choice. At the end of the day, we must do both, and they are not irreconcilable. We have to find a way to ensure that we support the entirety of the Constitution – that was the intention of the framers of the Constitution, and thats what we do on a daily basis at the National Security Agency. Still, the NSA has publicly acknowledged that it has inadvertently collected communications from some Americans without a warrant in the name of national security. It has not said how often that happens, though. Who Oversees the NSA Foreign Intelligence Surveillance Court Government surveillance agencies are also subject to review by the Privacy and Civil Liberties Oversight Board, which was created by Congress in 2004.

Principal Components and Factor Analysis

Principal Components and Factor Analysis Principal components analysis (PCA) and factor analysis (FA) are statistical techniques used for data reduction or structure detection. These two methods are applied to a single set of variables when the researcher is interested in discovering which variables in the set form coherent subsets that are relatively independent of one another. Variables that are correlated with one another but are largely independent of other sets of variables are combined into factors. These factors allow you to condense the number of variables in your analysis by combining several variables into one factor. The specific goals of PCA or FA are to summarize patterns of correlations among observed variables, to reduce a large number of observed variables to a smaller number of factors, to provide a regression equation for an underlying process by using observed variables, or to test a theory about the nature of underlying processes. Example Say, for example, a researcher is interested in studying the characteristics of graduate students. The researcher surveys a large sample of graduate students on personality characteristics such as motivation, intellectual ability, scholastic history, family history, health, physical characteristics, etc. Each of these areas is measured with several variables. The variables are then entered into the analysis individually and correlations among them are studied. The analysis reveals patterns of correlation among the variables that are thought to reflect the underlying processes affecting the behaviors of the graduate students. For example, several variables from the intellectual ability measures combine with some variables from the scholastic history measures to form a factor measuring intelligence. Similarly, variables from the personality measures may combine with some variables from the motivation and scholastic history measures to form a factor measuring the degree to which a stude nt prefers to work independently – an independence factor. Steps of Principal Components Analysis and Factor Analysis Steps in principal components analysis and factor analysis include: Select and measure a set of variables.Prepare the correlation matrix to perform either PCA or FA.Extract a set of factors from the correlation matrix.Determine the number of factors.If necessary, rotate the factors to increase interpretability.Interpret the results.Verify the factor structure by establishing the construct validity of the factors. Difference Between Principal Components Analysis and Factor Analysis Principal Components Analysis and Factor Analysis are similar because both procedures are used to simplify the structure of a set of variables. However, the analyses differ in several important ways: In PCA, the components are calculated as linear combinations of the original variables. In FA, the original variables are defined as linear combinations of the factors.In PCA, the goal is to account for as much of the total variance in the variables as possible. The objective in FA is to explain the covariances or correlations among the variables.PCA is used to reduce the data into a smaller number of components. FA is used to understand what constructs underlie the data. Problems with Principal Components Analysis and Factor Analysis One problem with PCA and FA is that there is no criterion variable against which to test the solution. In other statistical techniques such as discriminant function analysis, logistic regression, profile analysis, and multivariate analysis of variance, the solution is judged by how well it predicts group membership. In PCA and FA, there is no external criterion such as group membership against which to test the solution. The second problem of PCA and FA is that, after extraction, there is an infinite number of rotations available, all accounting for the same amount of variance in the original data, but with the factor defined slightly different. The final choice is left to the researcher based on their assessment of its interpretability and scientific utility. Researchers often differ in opinion on which choice is the best. A third problem is that FA is frequently used to â€Å"save† poorly conceived research. If no other statistical procedure is appropriate or applicable, the data can at least be factor analyzed. This leaves many to believe that the various forms of FA are associated with sloppy research.