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How Sociologists Use Statistics

How Sociologists Use Statistics. History. First called “political arithmetic” in 17 th century England and France and used to calculate population size and life expectancy. Those who conducted these studies were called “statists,” since the health of the “state” was at issue.

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How Sociologists Use Statistics

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  1. How Sociologists Use Statistics

  2. History • First called “political arithmetic” in 17th century England and France and used to calculate population size and life expectancy. • Those who conducted these studies were called “statists,” since the health of the “state” was at issue. • The first statistics in the U.S. were meant to inform debates over social issues in the 1830s such as prostitution.

  3. History (cont’d) • As a result of counting, “statists” recognized patterns: people’s actions were more or less stable over time. • During troubled times, the need to reveal these patterns were greater than ever before; facts were needed to guide social policy.

  4. Types of Statistics • --1. Descriptive (e.g., rates, percentages, averages):

  5. Poverty RatesUS Census 2006-2008 Milledgeville: all families: 19.1% Milledgeville: all people: 36.2% Baldwin County: all families: 14.8% Baldwin County: all people: 23.0% GA: all families: 11.0% GA: all people: 14.5% US: all families: 9.6% US: all people: 13.2%

  6. Criticisms of how the US Gov’t measures poverty • No adjustments for geographic differences • Exclusion of taxes and other government benefits • No way to account for differences in childcare and medical care costs • Inadequate system for adjusting for family size (Economic Policy Institute: www.epinet.org)

  7. Unemployment RatesGeorgia Department of Labor March 2010 • Baldwin County: 14.0% (or greater) • Georgia: 10.4% • National: 9.9%

  8. Criticisms of how the US Gov’t measures unemployment • The formula does not include those who want to work full-time but are discouraged and no longer looking for work. • It does not include those who have taken early retirement to avoid a layoff, but who prefer to be working.

  9. Neither does it include those who are “underemployed”: those who have part-time or seasonal work but who prefer to be employed full-time. • (wikipedia)

  10. Who is counted as unemployed? • Persons are classified as unemployed if they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work. Actively looking for work may consist of any of the following activities: • (http://www.bls.gov/cps/cps_htgm.htm#unemployed)

  11. Overweight/Obesity Rate • In 1998 public health agencies changed the indicators for overweight/obesity. • Normal: 18.5-24.9 • Overweight: From 27 to 25-29.9 • Obese: 30 or higher • Yet, in comparisons across time (e.g., from the 1980s to today), mention is rarely if ever made that those indicators have changed.

  12. My Criticism • We are comparing across time while using different indicators. • The Body Mass Index for the overweight/obese category has gotten lower, so this means that more people are placed in this category without gaining any weight.

  13. Changing Indicators • See “Food Calories and Land.” • See sociological images website. • See CDC website.

  14. Inferential Statistics • --2. Inferential Statistics: two examples: confidence interval, rejection of a hypothesis. • The research we’ll talk about today addresses both confidence interval and hypothesis testing in order to give us insight on “statistical significance.”

  15. Causality • A hypothesis such as “The higher one’s Body Mass Index (BMI) the less likely one will accept self” is a causal expression. • It suggests that self-acceptance is a function of or is caused by BMI. • In inferential statistical terminology that can be expressed as: the dependent variable (self-acceptance) is a function of one or more independent variables (BMI).

  16. Sociologists address this question with an analysis method called “regression.”

  17. Statistical significance in sociology is indicated with an asterisk. The more significant the regression coefficient is, the more asterisks that appear by the number. For example: -.34*** represents higher significance than -.21** (p. 254).

  18. How confident are we? • Three asterisks mean that there is only a 0.1% chance that this value represents a non-existent relationship. • This is indicated this way: p ≤ .001 • As statistics go, this is highly significant. The other common p values are: • p ≤ .05 (95% confidence interval or CI) • p ≤ .01 (99% CI)

  19. So…we can reject the null hypothesis that there is no statistically significant relationship between BMI and self-acceptance.

  20. Are there other types of significance? • Yes: substantive significance. • Substantive significance refers to the qualitative importance of the relationship. • “Statistical significance does not necessarily imply political, social, or economic significance. The relationship found may be so small – even though statistically significant – that the variable is of little consequence” (Schroeder, Sjoquist, and Stephan 1986).

  21. Acknowledgements • Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists by Joel Best (2001), University of California Press • Understanding Regression Analysis: An Introductory Guide by Larry D. Schroeder, David L. Sjoquist, and Paula E. Stephan (1986), Sage.

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