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Social statistics

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Use of statistical measurement systems to study human behavior in a social environment

Social statistics is the use ofstatistical measurement systems to studyhuman behavior in a social environment. This can be accomplished throughpolling 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.

Statistics in the social sciences

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History

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Adolph Quetelet published data on European population.

Adolph Quetelet was a proponent ofsocial physics. In his bookPhysique sociale[1] he presents distributions of humanheights,age of marriage, time of birth and death,time series of human marriages, births and deaths, asurvival density for humans and curve describingfecundity as a function of age. He also developed theQuetelet 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 andGeorge Udny Yule published "On theCorrelation of totalPauperism with Proportion ofOut-Relief" in 1895.[3]

A numericalcalibration for the fertility curve was given byKarl Pearson in 1897 in his "The Chances of Death, and Other Studies in Evolution"[4] In this book Pearson also usesstandard deviation,correlation andskewness for studying humans.

Vilfredo Pareto published his analysis of thedistribution of income inGreat Britain andIreland in 1897,[5] this is now known as thePareto principle.

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

Macroeconomic statistical research has providedstylized facts, which include:

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

Statistical methods in social sciences

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Diagram illustratingpath analysis: causal paths link endogenous variables and exogenous variables.
Cluster analysis showing two main clusters
A classification performed using theperceptron algorithm

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

Statistical techniques include:[9]

Covariance based methods

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Probability based methods

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Distance based methods

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Methods for categorical data

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Usage and applications

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Social scientists use social statistics for many purposes, including:

Reliability

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The use of statistics has become so widespread in the social sciences that many universities such asHarvard, have developed institutes focusing on "quantitative social science." Harvard's Institute for Quantitative Social Science focuses mainly on fields likepolitical science that incorporate the advanced causal statistical models thatBayesian methods provide. However, some experts in causality feel that these claims ofcausal statistics are overstated.[13][14] There is a debate regarding the uses and value of statistical methods in social science, especially inpolitical science, with some statisticians questioning practices such asdata 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 multiplelinear regression allow. Indeed, an important axiom that social scientists cite, but often forget, is that "correlation does not imply causation."

Further reading

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References

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  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, 1897https://archive.org/details/chancesdeathand00peargoog
  5. ^V. Pareto, Cours d'Économie Politique, vol. II, 1897
  6. ^Guttman, L. (1944). "A Basis for Scaling Qualitative Data".American Sociological Review.9 (20):603–611.doi:10.2307/2086306.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. ^abMiller, 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. ^abcHoffman, Frederick (1908). "Problems of Social Statistics and Social Research".Publications of the American Statistical Association.11 (82):105–132.doi:10.2307/2276101.JSTOR 2276101.
  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):223–235.doi:10.2307/2965000.JSTOR 2965000.
  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-bayesianhttp://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf

External links

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This article'suse ofexternal links may not follow Wikipedia's policies or guidelines. Pleaseimprove this article by removingexcessive orinappropriate external links, and converting useful links where appropriate intofootnote references.(November 2015) (Learn how and when to remove this message)
Social statistics at Wikipedia'ssister projects
Social science statistics centers
Statistical databases for social science
Continuous data
Center
Dispersion
Shape
Count data
Summary tables
Dependence
Graphics
Study design
Survey methodology
Controlled experiments
Adaptive designs
Observational studies
Statistical theory
Frequentist inference
Point estimation
Interval estimation
Testing hypotheses
Parametric tests
Specific tests
Goodness of fit
Rank statistics
Bayesian inference
Correlation
Regression analysis
Linear regression
Non-standard predictors
Generalized linear model
Partition of variance
Categorical
Multivariate
Time-series
General
Specific tests
Time domain
Frequency domain
Survival
Survival function
Hazard function
Test
Biostatistics
Engineering statistics
Social statistics
Spatial statistics
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