Social statistics

Template:Short description 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.

Statistics in the social sciencesEdit

HistoryEdit

File:Statue élevée à la mémoire de Adolphe Quetelet.jpg

Adolph Quetelet published data on European population.

Adolph Quetelet was a proponent of social physics. In his book Physique sociale<ref>A. Quetelet, Physique Sociale, https://archive.org/details/physiquesociale00quetgoog</ref> 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<ref>Template:Cite journal</ref> 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.<ref>Template:Cite journal</ref>

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"<ref>K. Pearson, The Chances of Death, and Other Studies in Evolution, 1897 https://archive.org/details/chancesdeathand00peargoog</ref> 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,<ref>V. Pareto, Cours d'Économie Politique, vol. II, 1897</ref> 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.<ref>Template:Cite journal</ref>

Macroeconomic statistical research has provided stylized facts, which include:

Bowley's law (1937) regarding the proportion between wages and national output <ref>A. Bowley, Wages and income in the United kingdom since 1860, 1937</ref>
The Phillips curve (1958) regarding the relation between wages and unemployment<ref>W. Phillips, The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957, published 1958</ref>

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 sciencesEdit

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Diagram illustrating path analysis: causal paths link endogenous variables and exogenous variables.

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Cluster analysis showing two main clusters

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A classification performed using the perceptron algorithm

Methods and concepts used in quantitative social sciences include:<ref name="MilSal">Template:Citation</ref>

Statistical techniques include:<ref name="MilSal"/>

Covariance based methodsEdit

Probability based methodsEdit

Distance based methodsEdit

Methods for categorical dataEdit

Usage and applicationsTemplate:AnchorEdit

Social scientists use social statistics for many purposes, including:

the evaluation of the quality of services available to a group or organization,
analyzing behaviors of groups of people in their environment and special situations,
determining the wants of people through statistical sampling
evaluation of wage expenditures and savings<ref name="Hoffman">Template:Cite journal</ref>
preventing industrial diseases<ref name="Hoffman"/>
prevention of industrial accidents<ref name="Hoffman"/>
labour disputes, such as supporting the Anthracite Coal Strike Commission of 1902-1903<ref>Template:Cite journal</ref>
supporting governments in times of peace and war<ref>Template:Cite journal</ref>

ReliabilityEdit

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.<ref>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.</ref><ref>J. Pearl, Bayesianism and causality, or, why I am only a half-bayesian http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf</ref> 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."