URBS 260- 06A Sampling.pdf

URBS 260 ANALYTICAL METHODS IN URBAN STUDIES 6A-SAMPLING TEACHER: DONNY SETO This Photo by Unknown author is licensed under CC BY-SA-NC. 2 TODAY’S AGENDA 3 KEY TERMS • Element or unit: a single case in the population • Population: all cases that a researcher is interested in • Sampling fra...

URBS 260 ANALYTICAL METHODS IN URBAN STUDIES 6A-SAMPLING TEACHER: DONNY SETO This Photo by Unknown author is licensed under CC BY-SA-NC. 2 TODAY’S AGENDA 3 KEY TERMS • Element or unit: a single case in the population • Population: all cases that a researcher is interested in • Sampling frame: the list of elements that the sample will be selected from • Sample: the elements (subset of a population) selected for investigation • Representative sample: a sample that contains the same essential characteristics as the population • Probability sample: a sample selected using a random process so that each element in the population has a known likelihood of being selected • Non-probability sample: a sample selected using a non-random method • Sampling error: estimation the error that occurs because of differences between the characteristics of the sample and those of the population • Non-response: when an element selected for the sample does not supply the required data • Census: data that comes from an attempt to collect information from all elements in the population 4 WHAT IS AN EXAMPLE OF SAMPLING USED IN THIS CLASS? 5 INTRODUCTION • Sampling: the selection of a subset of a population for research • Two main types of sampling: 1. Probability: Uses random selection methods, associated with quantitative methods 2. Non-probability: Does not use random selection methods, associated with qualitative research 6 SAMPLING ISSUES 7 8 9 TYPES OF PROBABILITY SAMPLE 10 PROBABILITY SAMPLE 1. Simple random sample 2. Systematic sample 3. Stratified random sample 4. Multi-stage cluster sampling 11 1- SIMPLE RANDOM SAMPLE Where each element has the same probability of being selected & each combination of elements has the same probability of being selected. To select a simple random sample: • Devise a sampling frame • A list of elements in the population • Number all the elements consecutively stating at 1 • Pick a sample size (n) from the total population (N) • Using a random number table or computer program to generate a list of random numbers • The sample will be comprised of the cases whose element numbers match the randomly generated numbers 12 1- SIMPLE RANDOM SAMPLE Sampling ratio • The sampling ratio = n/N • (sample size= n and population size = N) • a sample size (n) of 1000 taken from a population (N) of 100 000 • Sampling ratio is .01 (1000/100,000) 13 2- SYSTEMATIC SAMPLE For every ith case in the sampling frame is selected. •i = size of sampling interval • e.g., if you want to select every 30th case, i = 30 • To begin, choose a number at random from 1 to i, in this case from 1 to 30. • That number is known as a ‘random start’. • The case with that number is the first case selected. • Then select every ith case after that (in our case, every 30th case after that). Periodicity Issue with systematic sampling • This occurs if the cases in the sampling frame are arranged in some systematic order, e.g., in an election study: voter, non-voter, voter, non-voter, etc. • If we were to select every 30th case starting with case 20, we would select case 20, 50, 80, etc., i.e., all cases would be non-voters 14 3- STRATIFIED RANDOM SAMPLING This type of sampling ensures that subgroups in the population are proportionally represented in the sample. • e.g., assume you are doing a study of students and want to ensure that each faculty is represented in the sample proportionally. Assume a sampling ratio of 1 in 20 or .05. • The number of cases drawn from each faculty should equal 1/20 of all students in that faculty • That result is not guaranteed with a simple random or systematic sample 15 3- STRATIFIED RANDOM SAMPLE To select stratified random sample • Stratify the population, i.e., divide it into subgroups (in our example, into faculties). • Select a simple random sample or a systematic sample from each stratum. • In our case, the number of cases selected from a stratum would equal 1/20th of the number of people in that stratum. • Using this procedure ensures that each stratum (faculty) is proportionally represented in the total sample. • However, doing this is not always practicable. 16 4- MULTI-STAGE CLUSTER SAMPLING • Used for large populations. • No adequate sampling frame • Elements are geographically dispersed. • It involves two or more stages. • Selecting clusters (groups of elements) • Then selecting subunits within clusters HOW WOULD YOU GET A SAMPLE OF 17 THE CANADIAN POPULATION TO COLLECT A SURVEY ON? Discuss: • What are the pros and cons of your sampling method? • What can be done to improve on your first method? 18 19 HETEROGENEITY OF THE POPULATION • Generally, the greater the heterogeneity of the population on the characteristics of interest, the larger the sample size should be. 20 STOPPED HERE 21 KIND OF ANALYSIS • The sample size needed may vary depending on what sort of analysis will be done. • If small groups in the population are to be compared to larger groups, it may be necessary to oversample the smaller group in order to make meaningful comparisons. • Certain statistical procedures, such as some multivariate analyses, require large sample sizes to work properly. 22 TYPES OF NON-PROBABILITY SAMPLING 23 TYPES OF NON-PROBABILITY SAMPLING 1. Accidental sampling 2. Quota sampling 3. Theoretical Sampling 4. Purposive Sampling 5. Systematic Matching Sampling 6. Snowball sampling 24 1- ACCIDENTAL SAMPLING • Cases are included because they are readily available. • e.g., one could go to a mall and administer a survey to anyone willing to take part. • Problem: one cannot generalize the results to some larger population with any confidence • Convenience samples are useful for pilot studies, for testing the reliability of measures to be used in a larger study, for developing ideas, learning how do to research, etc. 25 2 - QUOTA SAMPLING • Collecting a specified number of cases in particular categories to match the proportion of cases in that category in the population. • e.g., there quotas for people in certain groups such as age, gender, ethnicity, class, etc. 26 3 - THEORETICAL SAMPLING • is a useful method of getting information from a sample of the population that you think knows most about a subject. A study on homelessness could concentrate on questioning people living on the street. This approach is common in qualitative research where statistical inference is not required. • 27 4 - PURPOSIVE SAMPLING • is where the researcher selects what he/she thinks is a ‘typical’ sample based on specialist knowledge or selection criteria. • 28 5- SYSTEMATIC MATCHING SAMPLING • used when two groups of very different size are compared by selecting a number from the larger group to match the number and characteristics of the smaller one. 29 5-SNOWBALL SAMPLING • The researcher makes contact with some individuals, who in turn provide contacts for other participants • e.g., students who participate in survey studies are asked to come up with the names of some non-students who may be willing to participate. 30 STRUCTURED OBSERVATION SAMPLING Often no sampling frame • e.g., a list of all people who were admitted to the emergency room at a particular hospital May involve time sampling • e.g. an emergency room may be observed at random times throughout the day May include place sampling • e.g. a study of student activities on campus may involve a sampling of places such as dining halls, pubs, classrooms, etc. May include behaviour sampling • e.g., a researcher may want to observe every fifth interaction between students and librarians at a particular reference desk 31 32 LIMITS TO GENERALIZATION • Even when a sample is selected using probability sampling, any findings can be generalized only to the population from which the sample was taken • Do the findings from an earlier date still apply today? 33 SAMPLE ISSUES IN RESEARCH DESIGN 34 SAMPLING PROBLEMS 35 SAMPLING PROBLEMS • Sampling error • Sampling related error • Arises from activities or events related to the sampling process, e.g., non-response, inadequate sampling frame, etc. 36 SAMPLING ERROR • Probability samples with sufficient sample sizes minimize the amount of sampling error, but some sampling error is bound to occur. • e.g., there is usually some difference between a sample mean( X ) and the population mean (μ) that it is designed to represent. • This sort of sampling error is measured by a statistic called the standard error of the mean. • About 95 per cent of all sample means lie within 1.96 standard errors of the mean. 37 STANDARD ERROR OF THE MEAN, CONT’D 38 SAMPLE SIZE • The absolute size of the sample matters • (not the proportion of the population that it comprises) • As sample size increases, sampling error tends to decrease. • Common sample sizes:100, 400, 900, 1600, 2500 • Each size increase cuts the sampling error by 1/2, then 1/3, then 1/4, and then 1/5 respectively. • The biggest change occurs between 100 and 400. • Is an increased sample size worth the time and effort? • Often sample size is dictated by financial concerns. 39 NON-RESPONSE • The response rate is the percentage of the sample that participates in the study. • Is there is some particular issue common to the nonresponders that brings them to differ in some important way from those who participate. 40 HETEROGENEITY OF THE POPULATION • Generally, the greater the heterogeneity of the population on the characteristics of interest, the larger the sample size should be. 41 PPAP – RAISE YOUR HAND IF YOU KNOW WHAT THIS IS? • A- 20-30 of age • B- Regular Facebook User • C- Regular Twitter User • D- Own a Smartphone • E- Are a Blogger • F- Have kids • What are some other factors that might be related to this topic? 42 PIKOTARO – PPAP 403 M VIEWS Retrieved From: https://www.youtube.com/watch?v=NfuiB52K7X8 43 DO YOU KNOW WHO KHABY LAME IS? 44 DO YOU KNOW WHO KHABY LAME IS?