The Logic of Sampling. Key Sampling Concepts Sampling (two types) Element Population Sample Sampling Frame Representative Sample.

Slides:

Advertisements

Similar presentations

Research Methods in Crime and Justice

Advertisements

Categories of Sampling Techniques n Statistical ( Probability ) Sampling: –Simple Random Sampling –Stratified Random Sampling –Cluster Random Sampling.

Beginning the Research Design

Sampling-big picture Want to estimate a characteristic of population (population parameter). Estimate a corresponding sample statistic Sample must be representative.

Research Methods Chapter 5: Sampling. Sampling Purpose: To draw enough of something to make your findings generalizable Purpose: To draw enough of something.

The Logic of Sampling. Political Polls and Survey Sampling In the 2000 Presidential election, pollsters came within a couple of percentage points of estimating.

Welcome to class of Sampling - Dr. Satyendra Singh.

7-1 Chapter Seven SAMPLING DESIGN. 7-2 Sampling What is it? –Drawing a conclusion about the entire population from selection of limited elements in a.

ISSUES RELATED TO SAMPLING Why Sample? Probability vs. Non-Probability Samples Population of Interest Sampling Frame.

Statistical Methods Descriptive Statistics Inferential Statistics Collecting and describing data. Making decisions based on sample data.

Chapter 8 Selecting Research Participants. DEFINING A POPULATION BY A RANDOM NUMBERS TABLE  TABLE 8.1  Partial Page of a Random Numbers Table  ____________________________________________________________________________.

SAMPLING Chapter 7. DESIGNING A SAMPLING STRATEGY The major interest in sampling has to do with the generalizability of a research study’s findings Sampling.

Sampling ADV 3500 Fall 2007 Chunsik Lee. A sample is some part of a larger body specifically selected to represent the whole. Sampling is the process.

Sampling Concepts Population: Population refers to any group of people or objects that form the subject of study in a particular survey and are similar.

I The Vocabulary of Sampling Statistical Control - Sampling II Types of Samples IV Determining Sample Size III Testing Samples.

Key terms in Sampling Sample: A fraction or portion of the population of interest e.g. consumers, brands, companies, products, etc Population: All the.

SAMPLING. EXTERNAL VALDITY The accuracy with which the result of an investigation maybe generalized to a different group from the one studied.

COLLECTING QUANTITATIVE DATA: Sampling and Data collection

University of Central Florida

Sampling January 9, Cardinal Rule of Sampling Never sample on the dependent variable! –Example: if you are interested in studying factors that lead.

Chapter XI Sampling: Design and Procedures. Chapter Outline Chapter Outline 1) Overview 2) Sample or Census 3) The Sampling Design Process i. Define the.

Sampling: Theory and Methods

Chapter 13 Data Sources, Sampling, and Data Collection.

Sampling Distribution

Foundations of Sociological Inquiry The Logic of Sampling.

Raymond Martin Lecture 7 – Sampling Data are collected to represent a population or study area. –A census is complete enumeration.

Introducing Communication Research 2e © 2014 SAGE Publications Chapter Eight Sampling: Who, What and How Many?

What are the Odds? Sampling Theory and Logic. Let’s Be Realistic… It’s unlikely you’ll be in a position to do much sampling in your daily work Important.

Slides to accompany Weathington, Cunningham & Pittenger (2010), Chapter 7: Sampling 1.

1 Hair, Babin, Money & Samouel, Essentials of Business Research, Wiley, Learning Objectives: 1.Understand the key principles in sampling. 2.Appreciate.

Sampling Methods.

McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Two THE DESIGN OF RESEARCH.

The Logic of Sampling. Methods of Sampling Nonprobability samplesNonprobability samples –Used often in Qualitative Research Probability or random samplesProbability.

Population and sample. Population: are complete sets of people or objects or events that posses some common characteristic of interest to the researcher.

MDM4U – Mathematics of Data Management

Chapter 7 The Logic Of Sampling. Observation and Sampling Polls and other forms of social research rest on observations. The task of researchers is.

1 UNIT 10: POPULATION AND SAMPLE. 2 Population The entire set of people, things or objects to be studied An element is a single member of the population.

Qualitative and quantitative sampling. Who are they Black/Blue/Green/Red Thin/Bold Smiling/Normal/Sad                        

Chapter 7 The Logic Of Sampling The History of Sampling Nonprobability Sampling The Theory and Logic of Probability Sampling Populations and Sampling Frames.

Sampling Neuman and Robson Ch. 7 Qualitative and Quantitative Sampling.

Chapter 6: 1 Sampling. Introduction Sampling - the process of selecting observations Often not possible to collect information from all persons or other.

Data Collection & Sampling Dr. Guerette. Gathering Data Three ways a researcher collects data: Three ways a researcher collects data: By asking questions.

Sampling Population: –All units (people or things) possessing the attributes and characteristics of interest (aggregation of study elements) E.g., Actors.

Chapter Twelve. Figure 12.1 Relationship of Sampling Design to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.

2-1 Sample Design. Sample Subset of a larger population Population Any complete group People Sales people Stores Students Teachers.

Chapter 7 The Logic Of Sampling.

Ch. 11 SAMPLING. Sampling Sampling is the process of selecting a sufficient number of elements from the population.

INFO 271B LECTURE 9 COYE CHESHIRE Sampling. Agenda Info 271B 2 Non-probability Sampling Probability Sampling Probability Distributions.

SAMPLING Why sample? Practical consideration – limited budget, convenience, simplicity. Generalizability –representativeness, desire to establish the broadest.

Copyright © 2011, 2005, 1998, 1993 by Mosby, Inc., an affiliate of Elsevier Inc. Chapter 13: Boundary Setting in Experimental-Type Designs A deductive.

Chapter 12 Vocabulary. Matching: any attempt to force a sample to resemble specified attributed of the population Population Parameter: a numerically.

Types of method Quantitative: – Questionnaires – Experimental designs Qualitative: – Interviews – Focus groups – Observation Triangulation.

Sampling. Census and Sample (defined) A census is based on every member of the population of interest in a research project A sample is a subset of the.

Sampling Concepts Nursing Research. Population  Population the group you are ultimately interested in knowing more about “entire aggregation of cases.

Sampling Dr Hidayathulla Shaikh. Contents At the end of lecture student should know  Why sampling is done  Terminologies involved  Different Sampling.

Sampling: Probability Vs Non-Probability

Types of Samples Dr. Sa’ed H. Zyoud.

Part Two THE DESIGN OF RESEARCH

Developing the Sampling Plan

Meeting-6 SAMPLING DESIGN

محيط پژوهش محيط پژوهش كه قلمرو مكاني نيز ناميده مي شود عبارت است از مكاني كه نمونه هاي آماري مورد مطالعه از آنجا گرفته مي شود .

Sampling-big picture Want to estimate a characteristic of population (population parameter). Estimate a corresponding sample statistic Sample must be representative.

MKT 416 Chapter 11: Sampling Design and Procedures

أدوات البحث العلمي: العينات

Types of Control I. Measurement Control II. Statistical Control

نمونه گيري و انواع آن تدوین کننده : ملیکه سادات ابراهیمی

Social Research Methods MAN-10 Erlan Bakiev, Ph. D

Sampling Methods.

Presentation transcript:

The Logic of Sampling

Key Sampling Concepts Sampling (two types) Element Population Sample Sampling Frame Representative Sample

Application of Sampling Terms

Types of Samples Probability -- Strictly following two rules. Non Probability -- Failing to follow the two rules

Types of Probability Samples Simple Random Sample (SRS) Systematic Random Sample Stratified Random Sample Cluster Sample

Simple Random Sample Every element has an equal chance of selection No element can be selected more than once

Example of a good random sample outcome

Simple Random Sample Every element has an equal chance of selection No element can be selected more than once

Systematic Random Sample Sampling Frame Nth Element A simple random sample employing a sample frame.

Stratified Random Sample

Cluster Sample

Non probability samples Convenience (available to researcher) Snowball (available connections) Quota (stratified without randomness) Informant (case study/social history) Focus Groups

What’s the difference? How important is the difference? Probability samples can be generalized to a population; while non-probability samples cannot. Non-probability offer an in depth understanding and are most often: “I don’t know what I am seeking until after I find it.” Following is an illustration:

Overview of Sample Problems Hite, S. (1987). Women in Love: A Cultural Revolution in Progress. NY: Alfred A. Knopf. Laumann, E. O., Gagnon, J. H., Michael, R. T. & Michaels, S. (1994). The Social Organization of Sexuality: Sexual Practices in the United States. Chicago: University of Chicago Press.

Sample Size Selection The problem with the following formula: It is calibrated for dichotomous data. The sample size will increase with the number of options given to the subject.

Do NOT Forget!!!! Regardless of what formula one uses, always increase the sample size by 20%.

Excel RANDBETWEEN – Returns a random number between the numbers you specify. RAND – Returns a random number greater than or equal to 0 and less than 1, evenly distributed (changes on recalculation).