Sampling

Why Sample? n Time, cost n Accuracy & representativeness n time-sensitive issues

What is a sample? Key Ideas & Basic Terminology n Sampling Guide (general introduction) in Reading Folder n Population, target population Population u the universe of phenomena we want to study u Can be people, things, practices n Sampling Frame (conceptual & operational issues) u how can we locate the population we wish to study? Examples: F Residents of a city? Telephone book, voters lists F Newsbroadcasts? Broadcast corporation archives? … F Telecommunications technologies?.... F Homeless teenagers? F “ethnic” media providers in BC (print, broadcast…)

Diagram of key ideas & terms

Target Population n Target Population--Conceptual definition: u the entire group about which the researcher wishes to draw conclusions. n Example Suppose we want to study homeless men aged who live in the downtown east side and are HIV positive. u The purpose of this study could be to compare the effectiveness of two AIDs prevention campaigns, one that encourages the men to seek access to care at drop-in clinics and the other that involves distribution of information and supplies by community health workers at shelters and on the street. u The target population here would be all men meeting the same general conditions as those actually included in the sample drawn for the study. n What sampling frames could we use to draw our samples?

Bad sampling frame = parameters do not accurately represent target population u e.g., a list of people in the phone directory does not reflect all the people in a town because not everyone has a phone or is listed in the directory.

Recall: Videoclip from Ask a Silly Question (play videoclip) n Ice Storm, electricity disruption, telephone survey n Target Population: Hydro company users n Sampling frame: unclear, probably phonebook or phone numbers of subscribers n Problem: people with no electricity not at home but in shelters n Famous examples from the past: Polls of voters before election (people with phones or car owners not representative of total voters, or opinions not yet formed)

More Basic Terminology n Sampling element (recall: unit of analysis) n e.g., person, group, city block, news broadcast, advertisement, etc…

Recall: Units of Analysis (Individuals)

Recall: Units of Analysis (Families)

( Households)

Recall: Importance of Choosing Appropriate Unit of Analysis for Research n Recall example: Ecological Fallacy (cheating) n Unit of analysis here is a “class” of students. Classes with more males had more cheating

What happens if we compare number and gender of cheaters? (unit of analysis “students”) n Do males cheat more than females? n Same absolute number of male and female cheaters in each class

Comparison of % and # of cheaters by gender

Recall: Ecological Fallacy & Reductionism ecological fallacy--wrong unit of analysis (too high) reductionism--wrong unit of analysis (too low)

More Basic Terminology n Sampling ratio u a proportion of a population F e.g., 3 out of 100 people F e.g., 3% of the universe

Factors Influencing Choice of Sampling Technique n Speed n Cost n Accuracy n Assumptions about distribution of characteristics of population n link to stats Can site _probability/non_probability.htm _probability/non_probability.htm _probability/non_probability.htm n Availability of means of access (sampling frame) n Nature of research question(s) & objectives

Some types of Non-probability Sampling 1. Haphazard, accidental, convenience (ex. “Person on the street” interview) 2. Quota (predetermined groups) 3. Purposive or Judgemental Deviant case (type of purposive sampling) 4. Snowball (network, chain, referral, reputation) & volunteer Also--multi-stage sampling designs

Non-probability Sampling 1. Haphazard, accidental, convenience (ex. “Person on the street” interview) Babbie (1995: 192)

Non-probability Sampling 2. Quota (predetermined groups) Neuman (2000: 197)

Why have quotas? n Ex. populations with unequal representation of groups under study u Comparative studies of minority groups with majority or groups that are not equally represented in population F Study of different experiences of hospital staff with technological change (nurses, nurses aids, doctors, pharmacists…different sizes of staff, different numbers)

Non-probability Sampling 3. Purposive or Judgemental n Unique/singular/particular cases u Hard-to-find groups u Leaders (“success stories”) n Range of different types

. Snowball (network, chain, referral, reputational) Non-probability Sampling 4. Snowball (network, chain, referral, reputational) Jim Anne Pat Peter Paul Jorge Tim Larry Dennis Edith Susan Sally Joyce Kim Chris Bob Maria Bill Donna Neuman (2000: 199) Sociogram of Friendship Relations

IssuesIssues in Non-probability sampling IssuesIssues in Non-probability sampling n Bias? n Is the sample representative? n Types of sampling problems: u Alpha: find a trend in the sample that does not exist in the population u Beta: do not find a trend in the sample that exists in the population

Types of Probability Sampling 1. Simple Random Sample 2. Systematic Sample 3. Stratified Sampling 4. Cluster Sampling See: Statistics Canada site

Simple Random Sample n With/without replacement? n Must take into account characteristics of population & sampling frame n Develop a sampling frame & Number sampling frame units n Select elements using mathematically random procedure u Table of random numbers u random number generator u Other statistical software n Link: How to use a table of random numbers Link: How to use a table of random numbers Link: How to use a table of random numbers

Principles of Probability Sampling n each member of the population an equal chance of being chosen within specified parameters n Advantages u ideal for statistical purposes n Disadvantages u hard to achieve in practice u requires an accurate list (sampling frame or operational definition) of the whole population u expensive

How to Do a Simple Random Sample n Develop sampling frame n Locate and identify selected element n Link to helpful website Link

2. Systematic Sample (every “n”th person) With Random StartSystematic Sample 2. Systematic Sample (every “n”th person) With Random StartSystematic Sample Babbie (1995: 211)

Problems with Systematic Sampling n Biases or “regularities” in some types of sampling frames (ex. Property owners’ names of heterosexual couples listed with man’s name first, etc…) n Urban studies example) rban studies example)rban studies example)

Other Types n Stratified Neuman (2000: 209)

ng Stratified Sampling: Sampling Disproportionately and Weighting Babbie (1995: 222)

Stratified Sampling n Used when information is needed about subgroups n Divide population into subgroups before using random sampling technique

Other Types n Cluster n When is it used? u lack good sampling frame or cost too high Singleton, et al (1993: 156)

Other Sampling Techniques (cont”d) n Probability Proportionate to Size (PPS) n Random Digit Dialing

New Technologies: Data Mining & the Blogosphere n Jan. 3, 2007 image with Boingboing as largest node (source: ning/2007/01/the_blogosphere.html) (source: ning/2007/01/the_blogosphere.html) (source: ning/2007/01/the_blogosphere.html)

Sample Size? n Statistical methods to estimate confidence intervals n Past experience (rule of thumb) n Smaller populations, larger sampling ratios n Other factors: n goals of study n number of variables and type of analysis n of populations n features of populations n In qualitative methods: notion of Saturation (Bertaux)

Examples of sampling issues & techniques n Survey about football (soccer) market (soccer) n Rural poverty project and sampling issues projectsamplingprojectsampling

Issues/notions in Probability SamplingProbability Issues/notions in Probability SamplingProbability n Assessing Equal chance of being chosen n Standard deviation n Sampling error n Sampling distribution n Central limit theorem n Confidence intervals (margin of error)

Techniques for Assessing Probability Sampling Probability Techniques for Assessing Probability Sampling Probability n Standard deviation n Sampling error n Sampling distribution n Central limit theorem n Confidence intervals (margin of error)

Inferences (Logic of Sampling) n Use data collected about probabilistic samples to make statistical inferences about target population n Note: inferences made about the probability (likelihood) that the observations were or were not due to chance