# Chapter 2 Samples and Populations

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Chapter 2 Samples and Populations
Sample vs. Population Design Methods Construction Errors

Population/Target Population
Sample vs. Population Population – the totality of subjects under consideration Target Population – consists of all subjects considered in the study Sample – a portion or a subset of the population for data collection and analysis Population/Target Population Sample

Sample vs. Population Population Target Population Sample Kalamazoo Young-adults and older House -holds

Census vs. Sample Survey
Census – collection of data using all subjects in the population Sample Survey – collection of data from a representative sample of the population Population/ Target Population Sample Note: Random Samples should be representative of the population

Study Design or Protocol Design
Steps involved in solving problems How do I solve this problem? ? ? Study design is done prior to data collection. It involves methods in data collection, analysis of the data and conclusions to be made.

Probability vs. Non-Probability Sampling
Probability Sampling – subjects are chosen by chance Non-probability Sampling – can be used for informal and less scientific studies Note: Non-probability sampling tend to be less representative of the target population

Methods in Probability Sampling
Simple Random Sampling (SRS) – samples are randomly selected from the population K-in-1 Systematic Sampling – Every kth subject is chosen Stratified Random Sampling – population is divided into subgroups called strata and SRS chosen from each strata Cluster sampling – population is divided into subgroups called clusters and clusters are randomly chosen as samples.

Example: Household Expenditures in Michigan
Target Population : Households in Michigan Simple Random Sampling – randomly selecting the sample from a list of households Systematic Sampling – every 10th household Stratified Sampling – take samples from each county Cluster Sampling – selecting counties in Michigan

Factors to be considered in a Survey
Money Time Content/Information

Face-to-face Interview Explain questions, explore issues, make observations, use visual aids Expensive, need interviewer training - at home or work Accuracy, better sampling Expensive - in public areas Cheaper, more people in less time Less representative sample Telephone Interview Accurate, cheap No personal observation Written Questionnaire Cheapest per respondent Bias from low response rate - by mail Allows anonymity Slow - by Cheaper, quicker results - web survey Quicker data processing Need computing expertise

Construction of Questionnaire
Is the question understandable? Are you gathering knowledge or attitude? Are the questions loaded? Do the questions ask for sensitive information? Note: An accurate answer leads to a good study and it starts from asking important questions correctly.

Types of Survey Errors Coverage errors – sampling frame excludes some segments of the target population Non-response errors – can cause bias in survey results Measurement errors – occurs when respondents answer ‘incorrectly’