Social statistics

Source: Wikipedia, the free encyclopedia.

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 sciences

History

Adolph Quetelet
published data on European population.

Quetelet Index
.

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.

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

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]

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.
Cluster analysis showing two main clusters
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

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
."

Further reading

References

  1. ^ A. Quetelet, Physique Sociale, https://archive.org/details/physiquesociale00quetgoog
  2. JSTOR 2979201
    .
  3. 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. 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. ^
    ISBN 0-7619-2046-3{{citation}}: CS1 maint: multiple names: authors list (link
    )
  10. ^
    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. 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-bayesian http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf

External links

Social science statistics centers
Statistical databases for social science
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