04:192:300 Lecture Notes - Lecture 5: Nonprobability Sampling, Alf Landon, Simple Random Sample

• School: Rutgers University
• Department: Communication
• Course: 04:192:300
• Professor: M E A R A Faw

Document Summary

Chose participants from phone directories & automobile registries a. ii. a. iii. Reality: roosevelt won-- by a landslide: what went wrong with their prediction, drawing samples, population a. i. a. ii. Every possible element of pre-defined aggregate that could be studied. A complete list of persons/objects we want to study a. ii. 1. Ex. census; class roster; etc: sample b. i. A subset of population: how can sampling methods affect generalizability, key concepts in sampling, sample: representative subset of a population a. i. a. ii. Sampling frame: the list from which all members of a population are sampled a. i. 1. Sampling error: degree to which a sample"s characteristics differ from the population"s characteristics a. ii. 1. a. ii. 2. Got in the way of the literary digest poll. How close does our sample match the actual characteristics of the larger population: representativeness: how closely a sample matches its population in terms of the characteristics we want to study b. i. We know a sampling error is low bc our representativeness is high b. ii. b. iii.