NOTES_Chapter 5_Data Collection and Sampling

NOTES_Chapter 5_Data Collection and Sampling - •...

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Chapter 5: Data Collection and Sampling 5.1 Methods of Collecting Data Direct Observation Experiments Surveys o Personal Interview o Telephone Interview o Self-Administered Survey o Questionnaire Design 1. Questionnaire should be as short as possible 2. Questions should be short, simple and clearly worded 3. Begin with demographic questions to help respondents get started and become comfortable quickly 4. Dichotomous questions (yes/no) can have shortcomings. Multiple choice may not offer all options 5. Open-ended questions are insightful but are time consuming and difficult to tabulate and analyze 6. Avoid using leading questions 7. Pretest questionnaire to uncover potential problems 8. Think about how you intend to tabulate and analyze responses – interval, nominal, or ordinal. Which type of statistical techniques―descriptive or inferential―you intend to apply to the data collected 5.2 Sampling Target Population Sample Population
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Unformatted text preview: • Self-selected Samples – almost always biased 5.3 Sampling Plans • Simple Random Sample – a sample selected in such a way that every possible sample with the same number of observations is equally likely to be chosen • Stratified Random Sampling – obtained by separating the population into mutually exclusive sets, or strata, and then drawing simple random samples from each stratum • Cluster Sample – a simple random sample of groups or clusters of elements • Sample Size – the larger a sample size the more accurate we can expect our results to be 5.4 Sampling and Nonsampling Errors • Sampling Error – refers to differences between the sample and the population that exists only because of the observations that happened to be selected for the sample • Nonsampling Error 1. Errors in data acquisition 2. Nonresponse Error 3. Selection Bias...
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This note was uploaded on 11/09/2011 for the course ECO 220 taught by Professor Tanaka during the Fall '11 term at University of Toronto- Toronto.

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