Bpp week 7 sampling methods
Upcoming SlideShare
Loading in...5
×
 

Bpp week 7 sampling methods

on

  • 1,648 views

Overview of key concepts and methods of samlping

Overview of key concepts and methods of samlping

Statistics

Views

Total Views
1,648
Views on SlideShare
1,626
Embed Views
22

Actions

Likes
0
Downloads
52
Comments
0

2 Embeds 22

https://bpp.blackboard.com 21
https://www.edu-apps.org 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

CC Attribution-ShareAlike LicenseCC Attribution-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Bpp week 7 sampling methods Bpp week 7 sampling methods Presentation Transcript

  • International Pre-Masters Diploma in Business Studies.International Pre-Masters Diploma in Legal Studies.Project & Report Session 7: Introduction to Sampling methods Liam Greenslade Professional Education: developing your career
  • Learning Objectives Explain the role of sampling in the research process Distinguish between probability and nonprobability sampling Understand the factors to consider when determining sample size Understand the steps in developing a sampling plan
  • Formulation of the Research Project A. Selecting the Appropriate Method B. Selecting the Participants C. Selecting Measurement Methods & Techniques D. Selecting Instrumentation
  • Sampling Sampling is the process of selecting a small number of elements from a larger defined target group The information gathered from the small group will allow judgments to be made about the larger groups We aim to obtain samples which are representative So that our data is generalisable
  • What is a sample? A sample is a segment of the population we wish to study A great deal of care is needed in selecting the sample so that we can say that the data we obtain from it is meaningful For these reasons social scientists have a number of sampling methods Some sampling methods are very powerful and enable us to make predictions about large populations from knowledge relatively few members
  • Basics of Sampling TheoryPopulation Element Defined target population Sampling unit Sampling frame
  • Key Definitions Population – group of things (people) having one or more common characteristics Sample – representative subgroup of the larger population  Used to estimate something about a population (generalize)  Must be similar to population on characteristic being investigated
  • Target population As well as defining our topic and formulating our research question, aim or hypothesis, we have also to decide who we want to study This is the target population Most of the time our target population will be too large to include all the potential participants What we do then is select a sample of people from the target population
  • Unit of analysis The unit of analysis is the entity that you are analyzing in your study. Any of the following could be a unit of analysis in a study: Individuals Groups (families, households,) Artifacts (books, photos, newspapers) Geographical units (town, census tract, state) Social interactions (dyadic relations, divorces, arrests)
  • Unit of Observation Unit of observation is the unit on which data are collected. It is the chosen analysis that determines what the unit of analysis is. For example, a survey may collect data on individuals. That data may include the individuals addresses. Then, in a later analysis, those data can be used to aggregate information to the household level so that the unit of analysis can be the household
  • The sampling frame The sampling frame is the list of ultimate sampling unit, which may be people, households, organizations, or other units of analysis. The list of registered students may be the sampling frame for a survey of the students at BPP Telephone directories are often used as sampling frames, for instance, but tend to under-represent the poor (who have fewer or no phones) and the wealthy (who have unlisted numbers).
  • Find a sampling frame How would you obtain a sampling frame for the following target populations? Business Students at BPP College New mothers in the Ealing area Households containing 5 or more people in Hammersmith Members of Millwall FC Supporters Club Car dealerships in Stratford
  • Size Matters  The larger your sample size, the more sure you can be that their answers truly reflect the population.  This indicates that for a given confidence level (CL), the larger your sample size, the smaller your confidence interval (CI).  However, the relationship is not linear (i.e., doubling the sample size does not halve the confidence interval).
  • Sampling ErrorSampling error is any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size
  • Factors to Consider in Sample DesignResearch objectives Degree of accuracy Resources Time frame Knowledge of target population Research scope Statistical analysis needs
  • Determining Sample Size How many completed questionnaires do we need to have a representative sample? Generally the larger the better, but that takes more time and money. Answer depends on:  How different or dispersed the population is.  Desired level of confidence.  Desired degree of accuracy.
  • Factors Affecting Sample Size for Probability Designs Variability of the population characteristic under investigation Level of confidence desired in the estimate Degree of precision desired in estimating the population characteristic
  • Common Methods for Determining Sample Size Common Methods:  Budget/time available  Executive decision  Statistical methods  Historical data/guidelines  Sample size calculator http://www.surveysystem.com/sscalc.htm
  • Calculating sample size You need to know: The size of the sample frame The confidence interval (CI) The confidence level (CL) http://www.surveysystem.com/sscalc.htm offers a free sample size calculator
  • Defining Population of Interest Population of interest is entirely dependent on Management Problem, Research Problems, and Research Design. Some Bases for Defining Population:  Geographic Area  Demographics  Usage/Lifestyle  Awareness
  • Types of sampling Sampling methods fall into 2 categories Probability sampling in which elements are selected using a random method All elements have an equal likelihood of selection We are able to estimate error for our sample statistics using probability theory Non-probability sampling does not involve random selection With non-probability samples we may or may not represent the population well, and it will often be hard for us to know how well weve done so.
  • Types of Sampling MethodsProbability Sampling Non-Probability Simple random Sampling sampling  Deliberate (quota) Stratified random sampling sampling  Convenience Systematic sampling sampling  Purposive Cluster sampling sampling Multistage sampling
  • Random Sampling In a random sample all members of the population have an equal chance of being selected This can be done by lottery Or it can be done using a random number table
  • Stratified Sample Sometimes we need to ensure that certain characteristics of our population are represented in the sample (e.g. gender) Stratifying our sample enables us to ensure that they are included We divide the sample into the groups we’re interested in and then sample from the subgroups
  • Stratified sample activity You have been appointed to lead a research team assigned with the task of finding the reasons teenagers smoke. The team has decided to conduct a nation-wide survey involving students between 14-16 years of age in secondary schools. Explain how you plan to draw the sample of students using stratified sampling. What subgroups would you include? What further information do you need to draw a representative sample?
  • Systematic Sampling  Technique  Use “system” to select sample (e.g., every 5th item in alphabetized list, every 10th name in phone book)  Advantage  Quick, efficient, saves time and energy  Disadvantage  Not entirely bias free; each item does not have equal chance to be selected  System for selecting subjects may introduce systematic error  Cannot generalize beyond pop actually sampled
  • Steps in Drawing a Systematic Random Sample 1: Obtain a list of units that contains an acceptable frame of the target population 2: Determine the number of units in the list and the desired sample size 3: Compute the skip interval 4: Determine a random start point 5: Beginning at the start point, select the units by choosing each unit that corresponds to the skip interval
  • Cluster Sampling Appropriate when you can‟t obtain a list of the members of the population have little knowledge of pop characteristics Population is scattered over large geographic area Works on the principle that similar elements are often physically clustered together (e.g in neighbourhoods or electoral wards)
  • Multistage Cluster Sampling  Stage 1  randomly sample clusters (schools)  Stage 2  randomly sample individuals from the schools selected
  • Deliberate (Quota) Sampling  Similar to stratified random sampling  Technique  Quotas set using some characteristic of the population thought to be relevant (e.g. class, gender, ethnicity)  Participants selected non-randomly to meet quotas  Disadvantage  selection bias  Cannot always set quotas for all characteristics important to study
  • Volunteer samples  People literally volunteer themselves to participate in the study  Usually in response to an advert, a letter or other inducement  Self-selecting samples can be a problem  Voters in the X factor or magazine polls are typically „self-selecting‟ samples
  • Convenience/Opportunitysampling Convenience sampling is sampling which involves the sample being drawn from that part of the population which is close to hand. Convenience sampling is used in research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. Convenience sampling often leads to a biased study since it consists of only available people.
  • Convenience Sampling “Take them where you find them” - nonrandom Intact classes, volunteers, survey respondents (low return), a typical group, a typical person Disadvantage: Selection bias Use post hoc analysis to show groups were equal at the start
  • Purposive Sampling Purposive sampling (criterion-based sampling)  Establish criteria necessary for being included in study and find sample to meet criteria Solution: Screening  Use random sampling to obtain a representative sample of larger population and then those subjects that are not members of the desired population are screened or filtered out  E.g. want to study smokers but can‟t identify all smokers
  • Sample Size  Critical factor is whether sample is representative  Necessary sample size depends on population size  Recommendations:  Use tables from books  30 per group  Descriptive studies – 10-20% of population  No more than 50% of population  Statistical power  Attrition
  • When Selecting Subjects … Are subjects with special characteristics necessary for your research? (age, gender, trained/untrained, expert/novice, size, etc.) Can you obtain the necessary permission and cooperation from the subjects? Can you find enough subjects? Interaction among selection of subjects, treatments, and measures is essential for experimental studies.
  • Developing a Sampling Plan1. Define the Population of Interest2. Identify a Sampling Frame (if possible)3. Select a Sampling Method4. Determine Sample Size5. Execute the Sampling Plan
  • CONFIDENCE LEVELS The confidence level tells you how sure you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers use the 95% confidence level.
  • CONFIDENCE INTERVALS The confidence interval is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer.
  • Reporting Subjects State how many subjects were selected Describe how the subjects were selected Discuss whether any subjects were lost during the study and why Explain why the subjects were selected Describe subject characteristics that are pertinent to study – be very specific Identify procedures taken to protect the subjects
  • CI & CL When you put the confidence level and the confidence interval together, you can say that you are 95% sure that the true percentage of the population is between 43% and 51%. The wider the confidence interval you are willing to accept, the more certain you can be that the whole population answers would be within that range. For example, if you asked a sample of 1000 people what type of beer they preferred and 60% said Brand A, you can be very certain that between 40% and 80% of all the people in the city actually do prefer that brand, but you cannot be so sure that between 59 and 61% of the people in the city prefer the brand.