sampling: an introduction

SamplingPrepared by:Karla Maolen B. Visbal

Definition

Sampling is a technique wherein only a part of the universe is studied and conclusions are drawn on that basis for the entire universe (Uppreti & Sirgh, 2006).

Statistical Population

A set of entities concerning which statistical inferences are to be drawn, based on a random sample taken from a population.

A subpopulation is a subset of a population.

Universe (theoretical sample population)

Population(empirical sample population)

Original Sample

Loss (non-response)

Final Sample (data)

Sample Size

An appropriate number size is crucial to any well-planned research investigation.

The question has no definite answer value due to many factors.

Types of Sampling

Probability Sampling Likelihood of any member of the population from

being included in the sample. Involves random sampling methods

Non-probability Sampling Purposive Sampling: The researcher chooses the

sample based on who they think would be appropriate for the study.

Does not involve random selection

Methods of Sampling

Probability Sampling

Simple Random

Systemic Random Stratified Cluster

Sampling

Simple Random

Simple Random Sample or SRS is a subset of individuals (sample) chosen from a larger set (a population) (Yates, 2008).

Individuals (n) are randomly chosen, in such a way that every set of n individuals has an equal chance of being the sample actually selected (Calkins, 1995-2005).

Simple Random

SRS with replacement: Each observation in the data set has an equal chance to

be selected. Can be selected over and over again

Simple Random

SRS with replacement:

Simple Random

SRS without replacement: In a simple random sample without replacement each

observation in the data set has an equal chance of being selected.

Once selected it can not be chosen again.

Simple Random

SRS without replacement:

Systematic Random

Every nth member of the population is sampled (Calkins, 1995-2005).

# Name of Hospital in Manila1 Amisola Maternity Hospital - Hermosa Street, Manuguit, Tondo2 Canossa Health and Social Center Foundation, Inc. - E. Jacinto Street, Magsaysay Village, Tondo3 Chinese General Hospital and Medical Center - Blumentritt Street, Santa Cruz4 Clinica Arellano General Hospital - Doroteo Jose Street, Santa Cruz5 De Ocampo Memorial Medical Center - Nagtahan Street, Santa Mesa6 Dr. Jose Fabella Memorial Hospital - Lope de Vega Street, Santa Cruz7 Dr. Mirando Unciano, Sr. Medical Center - V. Mapa Street, Santa Mesa8 Esperanza Health Center - Santa Mesa9 F. Lanuza Health Center and Lying-in Clinic - Alvarez Street, Santa Cruz10 GAT Andres Bonifacio Memorial Medical Center - Delpan Street, Tondo11 Hospital of the Infant Jesus - Laong Laan Street, Sampaloc12 Jose R. Reyes Memorial Medical Center - San Lazaro Compound, Rizal Avenue, Santa Cruz13 Manila Doctors' Hospital - United Nations Avenue, Ermita14 Maria Clara Health Center and Lying-in Clinic - Maria Clara corner Prudencio Streets, Sampaloc15 Mary Chiles General Hospital - Dalupan Street, Sampaloc16 Mary Johnston Hospital - Juan Nolasco Street, Tondo17 Medical Center Manila[1] - General Luna Street, Ermita18 Metropolitan Medical Center - Masangkay Street, Tondo19 Nephrology Center of Manila - San Andres Street corner Leon Guinto Street, Malate20 Ospital ng Maynila Medical Center - Quirino Avenue corner Roxas Boulevard, Malate

Stratified Sampling The population is divided into two or more strata and

each subpopulation is sampled.

Gender and age groups would be commonly used strata.

Each stratum must share the same characteristic.

Random sampling may be used to select a certain number of data points from each stratum.

Stratified Sampling Strategies

1. Using sampling fraction in each strata that is proportional to that of the total population.

a. Ex: 60% male and 40% female in a population; 3 males and 2 females/strata

2. Optimum Allocation/Disproportionate Allocation. In sampling units with differing sizes, larger units are more likely to be sampled than the smaller ones.

Cluster Sampling

A population is divided into clusters and a few of these (often randomly selected) clusters are exhaustively sampled.

Clusters are natural or predefined groups (e.g. families, classrooms, schools, etc.)

Cluster Sampling

Example: How many bicycles are owned in a community of 10,000 households?

o From the 500 blocks in the whole community, take 20 blocks, with 20 households each.

o Sample every household.

Cluster Sampling

One-Stage Cluster Sampling When a researcher includes all of the subjects from the

chosen clusters into the final sample

Multi-Stage Cluster Sampling Instead of using all the elements contained in the

selected clusters, the researcher randomly selects elements from each cluster.

Stage 1: Constructing Clusters Stage 2: Defining elements

Methods of Sampling

Non-Probability Sampling

Theoretical Snowball Quota Convenience

Theoretical Sampling

Refers to the process of choosing new research sites or cases to compare with one that have already been studied.

Its purpose is to gain a deeper understanding of analysed cases and facilitate the development of analytic frame and concepts used in their research.

Types of Snowball Sampling

Linear Researcher starts with one subject. Through referral,

the researcher only gets only one subject.


Exponential Non-Discriminative The first subject refers to multiple subjects. All

multiple subjects are sampled.


Exponential Discriminative Among the multiple referrals by the primary subjects

at each level, only one is chosen as the subject of research.

Snowball Sampling

Advantages Locate Hidden Population People located are

population specific

Disadvantages Community Bias Not Random Vague Population Size Wrong Anchoring

Quota Sampling

A population is first segmented into mutually exclusive subgroups. Judgment is used to select the target participants.

The researcher aims to represent the major characteristics of the population by sampling a proportional amount of each.

Quota Sampling

Example: Proportional Quota of 100 people is 40% women

and 60% men Sample 40 women and 60 men

Convenience Sampling

Or Sampling of Convenience is done as convenient, often allowing the element to choose whether or not it is sampled.

Be wary of convenience sampling because the data may be seriously biased.

Sampling ErrorsHarry S. Truman

33rd President of the United States

Samples only included rich, white people with a telephone in their homes.

Questions?

Thank YOU!

References:

Wilks, S. (1962). Mathematical Statistics). John Wiley.

Uppretti, D. & Sirgh, J. (2006). Encyclopedia of Statistics Volume 1. 1st Edition. Dominant Publishers and Distributors.

Trochim, W. (2006). Non-probability Sampling. http://www.socialresearchmethods.net/kb/sampnon.php. Retrieved on December 27, 2012.

Calkins, K. (1998-2005). Probability and Sampling/Distributions. http://www.andrews.edu/~calkins/math/edrm611/edrm07.htm#ERROR. Retrieved on December 27, 2012.

Yates, Daniel S.; David S. Moore, Daren S. Starnes (2008). The Practice of Statistics, 3rd Ed.

Bruin, J. 2006. newtest: command to compute new test. UCLA: Statistical Consulting Group.

Lorh, S. 1999. Sampling: Design and Analysis. Duxbury Press.

Charles C. Ragin, 'Constructing Social Research: The Unity and Diversity of Method', Pine Forge Press, 1994

References:

Barney G. Glaser & Anselm L. Strauss, 'The Discovery of Grounded Theory: Strategies for Qualitative Research', Chicago, Aldine Publishing Company, 1967

_______. Snowball Sampling. http://www.transtutors.com/homework-help/management/marketing/market-research/snowball-sampling/ Retrieved on January 7, 2013

Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP.

sampling: an introduction

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