1. Theory of Area Studies Week 13 Approaches to Area Studies 4 Reference ID00804 Mi-Ae Wartenbee ID00803 Hyun Sook Moon I22019 Li Yuan

2. Variable & Constants Variable In computer science and mathematics, a variable is a symbolic representation used to denote a quantity or expression. In applied statistics, a variable is a measurable factor, characteristic, or attribute of an individual or a system—in other words, something that might be expected to vary over time or between individuals. Variables are often contrasted with constants, which are known and unchanging. Constants A constant variable is a variable whose value cannot be changed once it is initially bound to a value. In other words, constant variables cannot be assigned to. In purely functional programming, all variables are constant, because there is no assignment. Usually the term constant is used in connection with mathematical functions of one or more variable parameters. These parameters, or other variables, are often called x, y, or z, using lowercase letters from the end of the Latin alphabet. Constants are, by convention, usually denoted by lowercase letters from the beginning of the Latin alphabet, such as a, b, and c.

3. Cross tabulation contingency tables A cross tabulation displays the joint distribution of two or more variables. They are usually presented as a contingency table in a matrix format. Whereas a frequency distribution provides the distribution of one variable, a contingency table describes the distribution of two or more variables simultaneously. Each cell shows the number of respondents that gave a specific combination of responses, that is, each cell contains a single cross tabulation. The following is a fictitious example of a 3 × 2 contingency table. In statistics, contingency tables are used to record and analyze the relationship between two or more variables, most usually categorical variables.

4. Partial correlation In probability theory and statistics, partial correlation measures the degree of association between two random variables, with the effect of a set of controlling random variables removed. Formally, the partial correlation between X and Y given a set of n controlling variables Z = {Z 1, Z 2, …, Z n }, written ρ XY·Z, is the correlation between the residuals R X and R Y resulting from the linear regression of X with Z and of Y with Z, respectively.

5. Comparative historical analysis Historical-comparative research is the marriage of two fields of sociology, historical sociology and comparative sociology (or the comparative method). Due to the combination of a historical perspective and a comparative method, historical comparative research applies social scientific research techniques from past events and questions. This research is then compared to other events in order to find positive relations that may have contributed or will contribute in the future to a similar or dissimilar results. Beginning in the late 1950’s, the discipline of history became more linked with sociology. Eventually historical sociology was accepted as a more concrete perspective during the 1970’s. Historical investigations are based on the remnants of the past called historical material, which include official documents, diaries and much more as is discussed below. Comparative sociology on the other hand, specifically looks at sociology across regions or nations. Historical comparative sociology differs from historical sociology by focusing only on three main issues. These issues are causal relationships, processes over time, and comparisons. It does not allow interpretive approaches, which historical sociology may favor in certain occasions. Major researchers: Darrington Moore, Jr., Charles Tilly and Theda Skocpol based their "theoretical insights" on Karl Marx, Weber and even Alexis de Tocqueville over Durkheim.

6. Comparative historical analysis Four major methods that researchers use to collect historical data: archival data, secondary sources, running records, and recollections. - The archival data, or primary sources, are typically the resources that researchers rely most heavily on. Archival data includes official documents and other items that would be found in archives, museums, etc. - Secondary sources are the works of other historians who have written history. Running records are “documentaries maintained by private or non profit organizations.” Finally recollections include sources such as autobiographies, memoirs or diaries. Three identifying issues: causal relationships, processes over time, and comparisons. John Stuart Mills: devised five methods by which people are able to systematically analyze their observations and make more accurate assumptions about causality. Mill's_Methods discusses; direct method of agreement, method of difference, joint method of agreement and difference, method of residues and method of concomitant variations. Some issues with this aspect of historical comparative research are that the Mill's methods are typically the most useful when the causal relationship is already suspected and can therefore be a tool for eliminating other explanations Mill's methods simply cannot provide proof that the variation in one variable was caused by the variation of another variable.

7. Factor analysis Factor analysis is a statistical data reduction technique used to explain variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are modeled as linear combinations of the factors, plus "error" terms. Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.

8. Parsimony Parsimony is a 'less is better' concept of frugality/economy/stinginess or caution in arriving at a hypothesis or course of action. Parsimony is also a factor in statistics: in general, mathematical models with the smallest number of parameters are preferred as each parameter introduced into the model adds some uncertainty to it Additionally, adding too many parameters leads to "connect-the-dots" curve-fitting which has little predictive power. In general terms, it may be said that applied statisticians (such as process control engineers) value parsimony quite highly, whereas mathematicians prefer to have a more predictive model even if a large number of parameters are required.