Sampling

Introduction to Sampling

Mari Jack

Image Source: http://www.socialresearchmethods.net/kb/sampterm.php

Types of Sampling

• Probability Sampling

• Nonprobability Sampling

Probability Sampling• Simple Random Sampling

Image source: http://ccelearn.csus.edu/wasteclass/images/randomSampling.jpghttp://www.shebreathes.com/.a/6a00d8341c627a53ef010536e1e60b970b-450wi

Probability Sampling

• Stratified Random Sampling

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Probability Sampling• Systematic Random Sampling

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Probability Sampling• Cluster (area) Random

Sampling

Probability Sampling• Multistage Sampling

Image source: http://www.ccrs.nrcan.gc.ca/glossary/images/0366_1.gif

Nonprobability Sampling• Accidental/haphazard or convenience

sampling

Image source: http://www.gseis.ucla.edu/~sa/ucla.gif

Nonprobability Sampling• Purposive Sampling

– Modal instance

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– Expert Sampling

Image source: http://ligon.wcpss.net/dept/images/kmiller_scientist.jpg


–Quota Sampling• Proportional• Nonproportional


– Heterogeneity Sampling

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– Snowball Sampling

Image source: http://www.persuasive.net/wp-content/uploads/2009/1/snowball-effect.jpg

ReferencesBabbie, Earl. The Practice of Social Research. Belmont: Wadsworth

Cengage Learning, 2009. Kerlinger, Fred N. Foundations of Behavioral Research. Fort Worth:

Harcourt Brace Jovanovich College Publishers, 1986.Nachmias, David and Chava Nachmias. Research Methods in the

Social Sciences. New York: St. Martin’s Press, 1976.Rosenthal, Robert and Ralph L. Rosnow. Essentials of Behavioral

Research: Methods and Data Analysis. New York: McGraw Hill Inc., 1991.

Trochim, William M. K. “Sampling” http://www.socialresearchmethods.net/kb/sampling.php Research Methods Knowledge Base (accessed November 11, 2009).

http://www.socialresearchmethods.net/kb/sampling.php

How big should your sample be?

•Rule of thumb: Bigger is better

•As n -> N, Confidence Interval -> 0

SE = s/√n

A handy graph

Or is a chart better?

But is it really that easy?

• All equations for n given N are based on parameters of the population such as the standard deviation

• Kaplan’s “paradox of sampling”• On the one hand, the sample is of no use if it is not truly

representative of its population, if it is not a “fair” sample. On the other hand, to know that it is representative, we must know what the characteristics of the population are, so that we can judge whether the sample reflects them properly; but in that case, we have no need of the sample at all.

• (Qtd. in Rosenthal 261)

Considerations for choosing sample size

• The degree of precision required between the sample and population• Less precision = smaller sample

• variability of the population• A more homogenous population requires a smaller sample

• method of sampling• A stratified sample requires fewer cases for accuracy

• way in which results will be analyzed• A smaller sample puts limits on types of analyses possible

(Powell 105-6)

Statistical Power• Power = 1 - Beta • Beta is the chance of error in

rejecting the alternative hypothesis when it is true or accepting the null hypothesis when it is false

• ideally power should be .99• usually settle for between .7 and .9

(Kraemer 10)

Considerations for choosing acceptable

Power• Specification of research goals in precise and realistic terms

• Identification of the design and measurement options available to address the research question

• Evaluation of the resources (time, personnel, and funding) available to the project.

(Kraemer 17)

How Power Effects Results

• Power dictates “Critical Effect Size”• a measure of how strong the theory

must minimally be to be “important to society”

• Can vary by field• Sometimes a case study of n=1 can

be important to society

(Kraemer 24)

Easy Methods to Avoid Doing the Math Yourself

• http://www.surveysystem.com/sscalc.htm

• http://www.ezsurvey.com/samplesize.html

• http://www.macorr.com/ss_calculator.htm

http://www.surveysystem.com/sscalc.htm

http://www.ezsurvey.com/samplesize.html

http://www.macorr.com/ss_calculator.htm

References• Frankfort-Nachmias, Chava and David Nachmias. Research

Methods in the Social Sciences. 7th ed. New York: Worth Publishers, 2008.

• Kraemer, Helena Chmura and Sue Thiemann. How Many Subjects? : Statistical Power Analysis in Research. Newbury Park, Calif.: Sage Publications, 1987.

• Powell, Ronald R. and Lynn Silipigni Connaway. Basic Research Methods for Librarians. Library and Information Science Text Series. 4th ed. Westport, Conn.: Libraries Unlimited, 2004.

• Rosenthal, Robert and Ralph L. Rosnow. Essentials of Behavioral Research : Methods and Data Analysis. 3rd ed. Boston: McGraw-Hill, 2008.

Sample Error and Sampling Bias

Terence Kratz – IS280 - 11/12/09

Sample Error

How close the sample size is to the population size, or how well a sample of that size approximates a given population.

What is it? The standard deviation of a sampling

distribution is referred to as the standard error or sampling error.

“Margin of Error” The greater your sample size, the smaller the

standard error. This is because the greater the sample size, the closer the sample is to the actual population itself.

Varies depending on what is being sampled.

Why should you care?

Depending on what claims are being made for what size population, the margin of error can indicate how strong the relationship being shown by the study actually is.

Sampling Bias

The error resulting from taking a non-random sample of a population

What is it? Based on sampling method used, some members of a

population are less likely to be included in the sample. May undermine the external validity of a test. Reduces the ability for results to be generalized to a

larger population. Some studies might deliberately take a biased sample

in order to produce misleading results. More often, sampling bias occurs because of the

inherent difficulty in obtaining a truly representative sample of a complex population.

Types of Sampling Bias

Selection from only a specific area of the population (intentional (“purposive”), or accidental “convenience sample”)

Self-selection bias Pre-screening of or advertising for volunteers

within particular groups Exclusion bias

Selection from a Specific Area

Biased if certain members are under-represented relative to others in the population being generalized to.

Biased if certain members are overrepresented relative to others in the population.

Appropriateness depends on the study and the population; also called “non-probability sampling”.

Pre-screening

Related to Selection from a Specific Area, but this form of bias occurs when participants are specifically screened for certain characteristics.

Often used intentionally to focus a study on segments of the population with certain traits (ex. medical drug trials).

May also include selecting only from certain kinds or groups of subjects in order to intentionally skew the sample toward a certain desired trait or characteristic.

Self-Selection Bias

Possible whenever the group of people being studied has any form of control over whether to participate.

Participants' decision to participate may be correlated with traits that affect the study, making the participants a non-representative sample.

People who have strong opinions or substantial knowledge may be more willing to spend time answering a survey than those who do not.

Self Selection Bias Cont.: Polls

Online and phone-in polls, which are biased samples because the respondents are self-selected. This means that people with strong opinions are more likely to respond to the poll than people who have less strong opinions (or feel indifferent). This tends to polarize responses by giving greater weight to segments of the population with extreme opinions.

Exclusion bias

Exclusion, often inadvertent, of any particular group or type of subjects from the sample.

e.x. exclusion of subjects who have recently migrated into the study area (population demographics have changed since the study was initiated).

Intentional exclusion is also sometimes used to screen out types of subjects that would normally be expected to be outliers in the study.

Literature Review

Mixed Method Sampling

Onwuegbuzie, Anthony J. and Kathleen M. T. Collins. “A Typology of Mixed Methods Sampling Designs in Social Science Research," The Qualitative Report 12, no. 2

(2007): 281-316

sam bloomberg-rissman

Mixed method sampling

Onwuegbuzie, Anthony J. and Kathleen M. T. Collins. “A Typology of Mixed Methods Sampling Designs in Social Science Research,"

The Qualitative Report 12, no. 2 (2007): 281-316

QUALITATIVE AND QUANTITATIVE METHODS USED

Generalizations



Sample Myths



Sample Method Selection



Sample Size?



Mixed Methods?



Concurrent? Sequential?



Representation and Legitimation



Ethical sampling



Conclusion



REFERENCES• Babbie, Earl. The Practice of Social Research. Belmont: Wadsworth Cengage Learning,

2009. • Frankfort-Nachmias, Chava and David Nachmias. Research Methods in the Social

Sciences. 7th ed. New York: Worth Publishers, 2008.• Kerlinger, Fred N. Foundations of Behavioral Research. Fort Worth: Harcourt Brace

Jovanovich College Publishers, 1986.• Kraemer, Helena Chmura and Sue Thiemann. How Many Subjects? : Statistical Power

Analysis in Research. Newbury Park, Calif.: Sage Publications, 1987.• Nachmias, David and Chava Nachmias. Research Methods in the Social Sciences. New

York: St. Martin’s Press, 1976.• Onwuegbuzie, Anthony J. and Kathleen M. T. Collins. “A Typology of Mixed Methods

Sampling Designs in Social Science Research," The Qualitative Report 12, no. 2 (2007): 281-316

• Powell, Ronald R. and Lynn Silipigni Connaway. Basic Research Methods for Librarians. Library and Information Science Text Series. 4th ed. Westport, Conn.: Libraries Unlimited, 2004.

• Rosenthal, Robert and Ralph L. Rosnow. Essentials of Behavioral Research : Methods and Data Analysis. 3rd ed. Boston: McGraw-Hill, 2008.

• Rosenthal, Robert and Ralph L. Rosnow. Essentials of Behavioral Research: Methods and Data Analysis. New York: McGraw Hill Inc., 1991.

• Trochim, William M. K. “Sampling” http://www.socialresearchmethods.net/kb/sampling.php Research Methods Knowledge Base (accessed November 11, 2009).

• Photos on slides 39-49 courtesy Sam Bloomberg-Rissman Photography

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Sampling