Social statistics

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.

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.

Statistics in the social sciences[]

History[]

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 function 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.

Macroeconomic statistical research has provided stylized facts, which include Bowley's law (1937) and the Phillips curve (1958).

Statistics and statistical analyses have become a key feature of social science: statistics is employed in economics, psychology, political science, sociology and anthropology. 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.^[5]^[6] 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.

Statistical methods in social sciences[]

Methods and concepts used in quantitative social sciences include:^[7]

Research design, survey methodology and survey sampling
Delphi method

Statistical techniques include:^[7]

Regression analysis
Classification analysis
Path analysis
Factor analysis
Bayesian statistics
Canonical correlation
Causal analysis
Cluster analysis
Cohort analysis
Linear discriminant analysis
Log-linear analysis
Probit and logit
Multidimensional scaling
Stochastic process
Structural Equation Modeling
Multilevel models
Latent class model
Item response theory

References[]

^ A. Quetelet, Physique Sociale, https://archive.org/details/physiquesociale00quetgoog
^ 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.
^ 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.
^ K. Pearson, The Chances of Death, and Other Studies in Evolution, 1897 https://archive.org/details/chancesdeathand00peargoog
^ 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.
^ J. Pearl, Bayesianism and causality, or, why I am only a half-bayesian http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf
^ Jump up to: ^a ^b Miller, Delbert C., & Salkind, Neil J (2002), Handbook of Research Design and Social Measurement, California: Sage, ISBN 0-7619-2046-3CS1 maint: multiple names: authors list (link)