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Lecture

# econ2500_-_chapter_3_-_pr.docx econ2500_-_chapter_3_-_pr.docx

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School
York University
Department
Economics
Course
ECON 2500
Professor
all
Semester
Winter

Description
Introductory Statistics for Economists ECON 2500 – Winter 2011 – Xianghong Li Chapter 3 – Producing Data – Feb 1 Introduction - Statistics is concerned with interpreting already available data as well as producing new data. - This chapter will help you understand: o Very basic principles of getting trustworthy data. o Develop a critical view of whether existing data use trustworthy or flawed. Data Sources - Limitation of anecdotal evidence. - Available data: from the library and the internet. - Producing data Introduction: Observational Study vs. Experiment - Observational study: observes individuals and measures variables of interest. Subjects (usually people) are not asked to do anything other than to respond truthfully to questions. - Experiment: deliberately imposes some treatment on individuals in order to observe their responses. Observational Study or Sampling - Used more frequently in the social sciences. - Examples include opinion polls, labour market activity surveys. - Usually, not everyone is in a sample. - A census includes each individual in the population. Such large samples are very expensive. Canada conducts a census every five years. 3.1 Experiments - Used more frequently in sciences and social experiment is less common. - Designating and actually conducting the experiment is important. - Experiment is not foolproof. Response can be confounded (contaminated) by lurking (overlooked) variables, and an well-intended experiment could provide misleading results. Design of Experiment – Concepts - Experimental units or subjects. - Treatment: experimental condition applied to a unit. - Response variables (outcomes). - Explanatory variables (factors). - Level of factors: each factor can take several values in an experiment and each value is a level for that factor. - Each treatment is formed by combining a specific value of each of the factors. Comparative Experiments: Importance of Having a Control Group - A design of a study is ‘biased’ if it systematically favours certain outcomes. - Control group: the group without treatment serving for comparison. This group will be put in the same environment (experiencing the same influence from lurking factors). How to Make the Treatment and Control Groups Comparable - Holding everything else constant, let the treatment be the only difference between the two groups. - Matching o Limitations: can only match on observed factor, e.g. education but not innate ability. - Randomization: rely on chance. o Especially useful to even out the influence from unobserved characteristics. o Limitation: may not be feasible. Principles of Experimental Design - Control - Randomize - Repeat Cautions about Experimentation - Double blind o Neither the subjects nor the people working with the subjects shall know which treatment any subject had received (avoid the knowledge of the treatment influencing the outcomes). - Strong influence of background variables. - Lack of realism. Refined Experimental Design – Blocking - Matched pairs (a special case, two subjects per block). - Block designs in general o Choosing similar units to form blocks. o Random assignment is carried out separately within each block. - Effects of
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