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Chapter 2 Samples and Populations. Sample vs. Population Design Methods Construction Errors. Sample vs. Population. Population – the totality of subjects under consideration Target Population – consists of all subjects considered in the study
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Chapter 2 Samples and Populations Sample vs. Population Design Methods Construction Errors
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 Sample Target Population 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
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’