Social statistics

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Social statistics is the use of statistical measurement systems to study human behavior in a social environment. This can be accomplished through polling a group of people, evaluating a subset of data obtained about a group of people, or by observation and statistical analysis of a set of data that relates to people and their behaviors.

Contents

Statistics in the social sciences

History

Adolph Quetelet, published data on European population. Statue elevee a la memoire de Adolphe Quetelet.jpg
Adolph Quetelet, published data on European population.

Adolph Quetelet was a proponent of social physics. In his book Physique sociale [1] he presents distributions of human heights, age of marriage, time of birth and death, time series of human marriages, births and deaths, a survival density for humans and curve describing fecundity as a function of age. He also developed the Quetelet Index.

Francis Ysidro Edgeworth published "On Methods of Ascertaining Variations in the Rate of Births, Deaths, and Marriages" in 1885 [2] which uses squares of differences for studying fluctuations and George Udny Yule published "On the Correlation of total Pauperism with Proportion of Out-Relief " in 1895. [3]

A numerical calibration for the fertility curve was given by Karl Pearson in 1897 in his "The Chances of Death, and Other Studies in Evolution" [4] In this book Pearson also uses standard deviation, correlation and skewness for studying humans.

Vilfredo Pareto published his analysis of the distribution of income in Great Britain and Ireland in 1897, [5] this is now known as the Pareto principle.

Louis Guttman proposed that the values of ordinal variables can be represented by a Guttman scale, which is useful if the number of variables is large and allows the use of techniques such as ordinary least squares. [6]

Macroeconomic statistical research has provided stylized facts, which include:

Statistics and statistical analyses have become a key feature of social science: statistics is employed in economics, psychology, political science, sociology and anthropology.

Statistical methods in social sciences

Diagram illustrating path analysis: causal paths link endogenous variables and exogenous variables. Path example.JPG
Diagram illustrating path analysis: causal paths link endogenous variables and exogenous variables.
Cluster analysis showing two main clusters. SLINK-density-data.svg
Cluster analysis showing two main clusters.
A classification performed using the perceptron algorithm. Perceptron cant choose.svg
A classification performed using the perceptron algorithm.

Methods and concepts used in quantitative social sciences include: [9]

Statistical techniques include: [9]

Covariance based methods

Probability based methods

Distance based methods

Methods for categorical data

Usage and applications

Social scientists use social statistics for many purposes, including:

Reliability

The use of statistics has become so widespread in the social sciences that many universities such as Harvard, have developed institutes focusing on "quantitative social science." Harvard's Institute for Quantitative Social Science focuses mainly on fields like political science that incorporate the advanced causal statistical models that Bayesian methods provide. However, some experts in causality feel that these claims of causal statistics are overstated. [13] [14] There is a debate regarding the uses and value of statistical methods in social science, especially in political science, with some statisticians questioning practices such as data dredging that can lead to unreliable policy conclusions of political partisans who overestimate the interpretive power that non-robust statistical methods such as simple and multiple linear regression allow. Indeed, an important axiom that social scientists cite, but often forget, is that "correlation does not imply causation." For example, it appears widely accepted that the lower numbers of women in decision making positions in politics, business and science is good evidence of gender discrimination. But where men suffer adverse statistical indicators such as greater imprisonment rates or a higher suicide rate, that is not usually accepted as evidence of gender bias acting against them.

Further reading

Related Research Articles

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Psychological statistics

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Quantitative research All procedures for the numerical representation of empirical facts

Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.

Spurious relationship Apparent, but false, correlation between causally-independent variables

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Confounding Variable in statistics

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Designing Social Inquiry: Scientific Inference in Qualitative Research is an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba that lays out guidelines for conducting qualitative research. The central thesis of the book is that qualitative and quantitative research share the same "logic of inference." The book primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research.

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David Collier (political scientist) American political scientist (born 1942)

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Andrew Gelman American statistician

Andrew Gelman is an American statistician, professor of statistics and political science at Columbia University. He earned a bachelor's degree in mathematics and in physics from MIT, where he was a National Merit Scholar, in 1986. He then earned a Ph.D. in statistics from Harvard University in 1990 under the supervision of Donald Rubin.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis

References

  1. A. Quetelet, Physique Sociale, https://archive.org/details/physiquesociale00quetgoog
  2. Edgeworth, F. Y. (1885). "On Methods of Ascertaining Variations in the Rate of Births, Deaths, and Marriages". Journal of the Statistical Society of London . 48 (4): 628–649. doi:10.2307/2979201. JSTOR   2979201.
  3. Yule, G. U. (1895). "On the Correlation of total Pauperism with Proportion of Out-Relief". The Economic Journal . 5 (20): 603–611. doi:10.2307/2956650. JSTOR   2956650.
  4. K. Pearson, The Chances of Death, and Other Studies in Evolution, 1897 https://archive.org/details/chancesdeathand00peargoog
  5. V. Pareto, Cours d'Économie Politique, vol. II, 1897
  6. Guttman, L. (1944). "A Basis for Scaling Qualitative Data". The American Sociological Review . 9 (20): 603–611. JSTOR   2086306.
  7. A. Bowley, Wages and income in the United kingdom since 1860, 1937
  8. W. Phillips, The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957, published 1958
  9. 1 2 Miller, Delbert C., & Salkind, Neil J (2002), Handbook of Research Design and Social Measurement, California: Sage, ISBN   0-7619-2046-3 {{citation}}: CS1 maint: multiple names: authors list (link)
  10. 1 2 3 Hoffman, Frederick (1908). "Problems of Social Statistics and Social Research". Publications of the American Statistical Association. 11 (82).
  11. Willcox, Walter (1908). "The Need of Social Statistics as an Aid to the Courts". Publications of the American Statistical Association. 13 (82).
  12. Mitchell, Wesley (1919). "Statistics and Government". Publications of the American Statistical Association. 16 (125).
  13. Pearl, Judea 2001, Bayesianism and Causality, or, Why I am only a Half-Bayesian, Foundations of Bayesianism, Kluwer Applied Logic Series, Kluwer Academic Publishers, Vol 24, D. Cornfield and J. Williamson (Eds.) 19-36.
  14. J. Pearl, Bayesianism and causality, or, why I am only a half-bayesian http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf
Social science statistics centers
Statistical databases for social science