econ408 population and sampling

Introduction Definition Types of Sampling Methods

Population and SampleECON408 (Reserach Methods in Economics)

Pairach Piboonrugnroj, PhD

Faculty of Economics, Chiang Mai Universityme (at) pairach (dot) com

2016

This course is a part of Bachelor of Economics at Chiang Mai University, Thailand

Pairach Piboonrugnroj, PhD Faculty of Economics, Chiang Mai University

ECON304 - 02. Index number

mailto:[email protected]


Outline

What we will learn in this topic

1 Introduction2 Definition3 Types of Sampling Methods




Introduction

what is the population and sample in research?

Write down your definition of populationand sample on a paper (2 minutes)

Discusswith a person next to you. Compare andcontrast your definitions (5 minutes)

Revise your definition if any

Share with the class




Definition of Population

Population is a complete set of elements (persons or objects)that possess some common characteristic defined by thesampling criteria established by the researcher

Composed of two groups - target population & accessiblepopulation

Target population (universe) is the entire group of people orobjects to which the researcher wishes to generalize the studyfindings. It meet set of criteria of interest to researcher

Accessible population is the portion of the population to whichthe researcher has reasonable access; may be a subset of thetarget population. May be limited to region, state, city, county,or institution

source: http://www.umsl.edu/ lindquists/sample.html




Definition of Samples

Terminology used to describe samples and sampling methodsare:

Sample = the selected elements (people or objects) chosen forparticipation in a study; people are referred to as subjects orparticipants

Sampling = the process of selecting a group of people, events,behaviors, or other elements with which to conduct a study

Sampling frame = a list of all the elements in the populationfrom which the sample is drawn

source: http://www.umsl.edu/ lindquists/sample.html




Sampling frame

Sampling frame could be extremely large if population isnational or international in nature. Frame is needed so thateveryone in the population is identified so they will have anequal opportunity for selection as a subject (element).Examples:

1 A list of all tourism companies that are the member of theChiang Mai Chamber of Commerce

2 A list of Economics students who are the member ofstudent association

3 A list of all children with disability who study in Chiang Mai




Sampling terminology

1 Randomization = each individual in the population has anequal opportunity to be selected for the sample

2 Representativeness = sample must be as much like thepopulation in as many ways as possible

3 Parameter = a numerical value or measure of acharacteristic of the population; remember P forparameter & population

4 Statistic = numerical value or measure of a characteristicof the sample; remember S for sample & statistic

5 Precision = the accuracy with which the populationparameters have been estimated; remember thatpopulation parameters often are based on the samplestatistics




Types of Sampling Methods

There are two main types of sampling methods:probability and non-probability

Either of both method shall be selected carefully




Probability Sampling Methods

Also called random sampling

Every element (member) of the population has aprobability greater than) of being selected for the sample

Everyone in the population has equal opportunity forselection as a subject

Increases sample’s representativeness of the population

Decreases sampling error and sampling bias




Types of probability sampling

Simple random

Stratified random

Cluster random sampling

Systematic




Types of probability sampling

Elements selected at random

Assign each element a number

Select elements for study by1 using a table of random numbers in book2 Computer generated random numbers table3 Draw numbers for box (hat)




Stratified random

Population is divided into subgroups, called strata, accordingto some variable or variables in importance to the study

Variables often used include: age, gender, ethnic origin,SES, diagnosis, geographic region, institution, or type ofcareTwo approaches to stratification - proportional &disproportional

1 Proportional = Subgroup sample sizes equal theproportions of the subgroup in the population

2 Disproportional = Subgroup sample sizes are not equal tothe proportion of the subgroup in the population




Cluster random sampling

A random sampling process that involves stages of sampling.The population is first listed by clusters or categories. Theprocedure are:

Randomly select 1 or more clusters and take all of theirelements (single stage cluster sampling); e.g. Northernregion of Thailand

Or, in a second stage randomly select clusters from thefirst stage of clusters; eg 3 provinces in Northern region ofThailand

In a third stage, randomly select elements from thesecond stage of clusters; e.g. 30 county health dept.nursing administrators from each state




Systematic

A random sampling process in which every kth (e.g. every 5thelement) or member of the population is selected for thesample after a random start is determined. Example:

Population (N) = 2000, sample size (n) = 50, k=N/n, so k = 2000 ) 50 =40

Use a table of random numbers to determine the starting point forselecting every 40th subject

With list of the 2000 subjects in the sampling frame, go to the startingpoint, and select every 40th name on the list until the sample size isreached. Probably will have to return to the beginning of the list tocomplete the selection of the sample.




Non-probability sampling methods

Issues to consider for Non-probability sampling methods.

Characteristics

Types of non-probability sampling methods




Characteristics

Not every element of the population has the opportunityfor selection in the sample

No sampling frame

Population parameters may be unknown

Non-random selection

More likely to produce a biased sample

Restricts generalization

Historically, used in most nursing studies




Types of non-probability sampling methods

Convenience - aka chunk, accidental & incidentalsampling

Quota

Purposive - aka judgmental or expert’s choice sampling

Snowball




Convenience - aka chunk, accidental & incidentalsampling

Selection of the most readily available people or objectsfor a study

No way to determine representativeness

Saves time and money




Quota

Selection of sample to reflect certain characteristics ofthe population

Similar to stratified but does not involve random selection

Quotas for subgroups (proportions) are established

E.g. 50 males & 50 females; recruit the first 50 men andfirst 50 women that meet inclusion criteria




Purposive - aka judgmental or expert’s choicesampling

Researcher uses personal judgement to select subjectsthat are considered to be representative of the population

Handpicked subjects

Typical subjects experiencing problem being studied




Snowball

Also known as network sampling

Subjects refer the researcher to others who might berecruited as subjects




Time Frame for Studying the Sample

See design notes on longitudinal & cross-sectional studies

Longitudinal

Cross-sectional




Sample Size

General rule - as large as possible to increase therepresentativeness of the sample

Increased size decreases sampling error

Relatively small samples in qualitative, exploratory, casestudies, experimental and quasi-experimental studies

Descriptive studies need large samples; e.g. 10 subjectsfor each item on the questionnaire or interview guide

As the number of variables studied increases, the samplesize also needs to increase in order to detect significantrelationships or differences

A minimum of 30 subjects is needed for use of the centrallimit theorem (statistics based on the mean)




Large samples are needed if:

There are many uncontrolled variables

Small differences are expected in the sample/populationon variables of interest

The sample is divided into subgroups

Dropout rate (mortality) is expected to be high

Statistical tests used require minimum sample orsubgroup size




Power Analysis (I)

Power analysis = a procedure for estimating either thelikelihood of committing a Type II error or a procedure forestimating sample size requirements.

Determine the sample size

Background Information for Understanding PowerAnalysis: Type I and Type II errors




Type I Error

Based on the statistical analysis of data, the researcherwrongly rejects a true null hypothesis; and therefore,accepts a false alternative hypothesisProbability of committing a type I error is controlled by theresearcher with the level of significance, alpha.Alpha a is the probability that a Type I error will occurAlpha a is established by researcher; usually a = .05 or .01Alpha a = .05 means there is a 5% chance of rejecting atrue null hypothesis; OR out of 100 samples, a true nullhypothesis would be rejected 5 times out of 100 andaccepted 95 times out of 100.Alpha a = .01 means there is a 1% chance of rejecting atrue null hypothesis; OR out of 100 samples, a true nullhypothesis would be rejected 1 time out of 100 andaccepted 99 times out of 100




Type II error

Based on the statistical analysis of data, the researcherwrongly accepts a false null hypothesis; and therefore,rejects a true alternate hypothesisProbability of committing a Type II error is reduced by apower analysis

1 Probability of a Type II error is called beta b2 Power, or 1- b is the probability of rejecting the null

hypothesis and obtaining a statistically significant result




Sampling Error and Sampling Bias

Sampling error = The difference between the sample statistic(e.g. sample mean) and the population parameter (e.g.population mean) that is due to the random fluctuations indata that occur when the sample is selected.Sampling bias:

Also called systematic bias or systematic variance

The difference between sample data and population datathat can be attributed to faulty sampling of the population

Consequence of selecting subjects whose characteristics(scores) are different in some way from the populationthey are suppose to represent

This usually occurs when randomization is not used




Randomization Procedures in Research

Randomization = each individual in the population has anequal opportunity to be selected for the sample

Random selection = from all people who meet theinclusion criteria, a sample is randomly chosen




Q&A



econ408 population and sampling

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