Sampling & Simulation Chapter 14. 14.1 – Common Sampling Techniques  For researchers to make valid inferences about population characteristics, samples.

Slides:



Advertisements
Similar presentations
MBF3C Lesson #1: Sampling Types and Techniques
Advertisements

Chapter 5 Producing Data
Selection of Research Participants: Sampling Procedures
© 2003 Prentice-Hall, Inc.Chap 1-1 Business Statistics: A First Course (3 rd Edition) Chapter 1 Introduction and Data Collection.
Chapter 1 The Where, Why, and How of Data Collection
AP Statistics Chapter 5 Notes.
Chapter 7 Sampling Distributions
© 2004 Prentice-Hall, Inc.Chap 1-1 Basic Business Statistics (9 th Edition) Chapter 1 Introduction and Data Collection.
Sampling Prepared by Dr. Manal Moussa. Sampling Prepared by Dr. Manal Moussa.
The Practice of Statistics
Basic Business Statistics (8th Edition)
Chapter 4 Selecting a Sample Gay, Mills, and Airasian
Sample Surveys Ch. 12. The Big Ideas 1.Examine a Part of the Whole 2.Randomize 3.It’s the Sample Size.
Jon Curwin and Roger Slater, QUANTITATIVE METHODS: A SHORT COURSE ISBN © Thomson Learning 2004 Jon Curwin and Roger Slater, QUANTITATIVE.
ASKING PEOPLE ABOUT THEMSELVES: SURVEY RESEARCH © 2012 The McGraw-Hill Companies, Inc.
Sample Design.
Chapter 3 Goals After completing this chapter, you should be able to: Describe key data collection methods Know key definitions:  Population vs. Sample.
McGraw-Hill/Irwin McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Homework Check Homework check now... Please take out your homework so we can check it.
Chapter 1: Introduction to Statistics
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
BPS - 5th Ed. Chapter 81 Producing Data: Sampling.
CHAPTER 12 – SAMPLING DESIGNS AND SAMPLING PROCEDURES Zikmund & Babin Essentials of Marketing Research – 5 th Edition © 2013 Cengage Learning. All Rights.
Chap 20-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 20 Sampling: Additional Topics in Sampling Statistics for Business.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
Data Collection Methods
1-3 Data Collection and Sampling Techniques Surveys are the most common method of collecting data. Three methods of surveying are: 1) Telephone surveys.
Collecting Samples Chapter 2.3 – In Search of Good Data Mathematics of Data Management (Nelson) MDM 4U.
Part III Gathering Data.
Population and Sampling
Aim: What are the types of surveys and sampling techniques used by researchers?
MDM4U - Collecting Samples Chapter 5.2,5.3. Why Sampling? sampling is done because a census is too expensive or time consuming the challenge is being.
Tahir Mahmood Lecturer Department of Statistics. Outlines: E xplain the role of sampling in the research process D istinguish between probability and.
Sampling Chapter 1. EQT 373 -L2 Why Sample? Selecting a sample is less time-consuming than selecting every item in the population (census). Selecting.
Chapter Five Data Collection and Sampling Sir Naseer Shahzada.
AP STATISTICS LESSON AP STATISTICS LESSON DESIGNING DATA.
Learning Objectives Explain the role of sampling in the research process Distinguish between probability and nonprobability sampling Understand the factors.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 7-1 Chapter 7 Sampling Distributions Basic Business Statistics.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 1-5 Collecting Sample Data.
Aim: Review Session 1 for Final Exploratory Data Analysis & Types of Studies HW: complete worksheet.
Notes 1.3 (Part 1) An Overview of Statistics. What you will learn 1. How to design a statistical study 2. How to collect data by taking a census, using.
Part III – Gathering Data
SAMPLING TECHNIQUES LECTURE - 2 GE 608 Experimental Methods and Analysis Oct 28, 2015 Muharrum 14, 1437.
Chapter 5 Sampling: good and bad methods AP Standards Producing Data: IIB4.
1 of 29Visit UMT online at Prentice Hall 2003 Chapter 1, STAT125Basic Business Statistics STATISTICS FOR MANAGERS University of Management.
Data Collection and Sampling Techniques.   Data can be collected in a variety of ways. One of the most common methods is through the use of surveys.
Chapter 3 Surveys and Sampling © 2010 Pearson Education 1.
Sampling technique  It is a procedure where we select a group of subjects (a sample) for study from a larger group (a population)
Status for AP Congrats! We are done with Part I of the Topic Outline for AP Statistics! (20%-30%) of the AP Test can be expected to cover topics from chapter.
1 Chapter 11 Understanding Randomness. 2 Why Random? What is it about chance outcomes being random that makes random selection seem fair? Two things:
© Copyright McGraw-Hill CHAPTER 14 Sampling and Simulation.
1.3 Experimental Design. What is the goal of every statistical Study?  Collect data  Use data to make a decision If the process to collect data is flawed,
Experimental Design Data Collection Sampling Techniques.
Chapter 14 Sampling and Simulation McGraw-Hill, Bluman, 7th ed., Chapter 14 1.
Data Collection. At the end of this lesson, the student should be able to:  1. recognize the importance of data gathering;  2. distinguish primary from.
Collecting Samples Chapter 2.3 – In Search of Good Data Mathematics of Data Management (Nelson) MDM 4U.
Lecture 5.  It is done to ensure the questions asked would generate the data that would answer the research questions n research objectives  The respondents.
How can data be used? Data can be used to:
AC 1.2 present the survey methodology and sampling frame used
Sampling.
Experimental Design, Data collection, and sampling Techniques
Probability and Statistics
Sampling and Surveys How do we collect data? 8/20/2012.
Lecture 2: Data Collecting and Sampling
Use your Chapter 1 notes to complete the following warm-up.
Chapter 5: Producing Data
Business Statistics: A First Course (3rd Edition)
Surveys and Questionnaire Design
Probability and Statistics
Presentation transcript:

Sampling & Simulation Chapter 14

14.1 – Common Sampling Techniques  For researchers to make valid inferences about population characteristics, samples MUST be random  Random sample  Every member of population has an equal chance of being selected  Unbiased sample  Sample is chosen at random from population, and is representative of population  Biased sample  Sample is selected incorrectly by some type of systematic error

Why Use a Sample?  Samples are used to get information about populations for several reasons 1. It saves researcher time and money 2. It enables researcher to get information that he or she might not be able to obtain otherwise 3. It enables researcher to get more detailed information about a particular subject

Random Sampling  Basic requirement  For any sample size, all possible samples of this size have an equal chance of being selected from the population  Incorrect Methods 1. Ask “the person on the street” – many people will be at home or at work and did not have a chance of being selected 2. Ask question by radio or television – only those who feel strongly about issue may respond, others will ignore 3. Ask for mail ( ) responses – only whose who are concerned or have time will respond

Random Sampling, cont.  Preferred way of selected random samples is to use random numbers  Computers and calculators can generate random numbers  Random samples can be selected with or without replacement  Random sampling has one limitation  Using random numbers for extremely large populations is time consuming

Systematic Sampling  Systematic sample  Sample obtained by numbering each element in population and then selecting every third or fifth or tenth, etc., number from population to be included in sample  First number is selected at random  Example 14 – 2  Using population of 50 states, select a systematic sample of 10 states

Systematic Sampling cont.  Advantage of systematic sampling  Ease of selecting sample elements  In many cases, a numbered list of population units may already exist  Disadvantage of systematic sampling  Be careful of how items are arranged on numbered list  (such as male/female selecting every 2 nd item)

Stratified Sampling  Stratified sample  Sample obtained by dividing population into subgroups, called strata, according to various characteristics and then selecting members from each stratum for sample  Example 14 – 3 page 725

Stratified Sampling cont.  Advantage  Ensures representation of all population subgroups that are important to study  Disadvantages  Dividing a large population into representative subgroups requires a great deal of effort  If variables are complex or ambiguous (beliefs, attitudes, etc.) then it is difficult to separate individuals into subgroups according to these variables

Cluster Sampling  Cluster sample  Sample obtained by selecting a preexisting or natural group, called a cluster, and using members in cluster for sample  Advantages  Reduce costs  Simple fieldwork  Convenient  Disadvantage  Elements in cluster may not have same variations in characteristics selected individually from population

Other Types of Sampling Techniques  Sequence sampling  Used in quality control, successive units taken from production lines to ensure products meet certain standards set by company  Double sampling  Large population is given questionnaire to determine who meets qualifications  Sample is selected from those who meet qualifications of survey  Multistage sampling  Researcher uses a combination of sampling methods

Conducting a Sample Survey  Steps for conducting a sample survey 1. Decide what information is needed 2. Determine how data will be collected 3. Select information gathering instrument or design questionnaire if one is not available 4. Set up sampling list, if possible 5. Select best method for obtaining sample 6. Conduct survey and collect data 7. Tabulate data 8. Conduct statistical analysis 9. Report results

14.2 – Surveys & Questionnaire Design  Survey is conducted when a sample of individuals is asked to respond to questions about a particular subject  Two types of surveys 1. Interviewer-administered 2. Self-administered

Interviewer & Self Administered Surveys  Interviewer administered  Require a person to ask questions  Can be conducted face to face or via telephone  Self administered  Can be done by mail ( ) or in group setting such as a classroom

Common Questionnaire Mistakes 1. Asking biased questions 2. Using confusing words 3. Asking double-barreled questions 4. Using double negatives in questions 5. Ordering questions improperly

How bias occurs…  Many people will make responses on basis of what they think person asking questions wants to hear  People will respond differently depending on whether their identity is known  Time and place where a survey is conducted can affect results  Closed-ended vs. open-ended questions

Other survey tips  Use a pilot study to test design and usage of questionnaire  Helps researcher to pretest questionnaire to determine if it meets objectives of the study  Helps researcher to rewrite any questions that may be misleading, ambiguous, etc.  Surveys sent by mail ( )  Cover letter  Clear directions

14.3 – Simulation Techniques and the Monte Carlo Method  Simulation technique  Uses a probability experiment to mimic a real-life situation  Actual situations may be too costly, dangerous, or time-consuming  Simulations are created to be less expensive, less dangerous, and less time- consuming

Computers and Simulation  Mathematical simulation techniques use probability and random numbers to create real-life conditions  Computers’ role in simulation  Generate random numbers  Perform experiments  Tally outcomes  Compute probabilities

Monte Carlo Method  Monte Carlo method  Simulation technique using random number  Used in business and industry  Steps for simulating experiments using Monte Carlo method: 1. List all possible outcomes of experiment 2. Determine probability of each outcome 3. Set up correspondence between outcomes of experiment and random numbers 4. Select random numbers from table and conduct experiment 5. Repeat experiment and tally outcomes 6. Compute any statistics and state conclusions

Examples  Example 14 – 4  Using random numbers, simulate the gender of children born  Example 14 – 5  Using random numbers, simulate the outcomes of a tennis game between Bill and Mike, with the additional condition that Bill is twice as good as Mike.

Remember…  Simulation techniques do not give exact results  Number of times experiment is performed  Closer actual results get closer to theoretical results (law of large numbers)