STK4600Statisticalmethodsforsocialsciences.ppt

STK 4600: Statistical methods for social sciences. Survey sampling and statistical demography Surveys for households and individuals Survey sampling: 4 major topics Traditional design-based statistical inference 6 weeks Likelihood considerations 1 weeks Model-based statistical inference 3 weeks Missing data - nonresponse 2 weeks Statistical demography Mortality Life expectancy Population projections 2-3 weeks Course goals Give students knowledge about: planning surveys in social sciences major sampling designs basic concepts and the most important estimation methods in traditional applied survey sampling Likelihood principle and its consequences for survey sampling Use of modeling in sampling Treatment of nonresponse A basic knowledge of demography But first: Basic concepts in sampling Population (Target population): The universe of all units of interest for a certain study Denoted, with N being the size of the population: U = {1, 2, ...., N} All units can be identified and labeled Ex: Political poll – All adults eligible to vote Ex: Employment/Unemployment in Norway– All persons in Norway, age 15 or more Ex: Consumer expenditure : Unit = household Sample: A subset of the population, to be observed. The sample should be ”representative” of the population Sampling design: The sample is a probability sample if all units in the sample have been chosen with certain probabilities, and such that each unit in the population has a positive probability of being chosen to the sample We shall only be concerned with probability sampling Example: simple random sample (SRS). Let n denote the sample size. Every possible subset of n units has the same chance of being the sample. Then all units in the population have the same probability n/N of being chosen to the sample. The probability distribution for SRS on all subsets of U is an example of a sampling design: The probability plan for selecting a sample s from the population: Basic statistical problem: