Journalism 614: Sampling

Sampling  Probability Sampling –Based on random selection  Non-probability sampling –Based on convenience

Sampling Miscues: Alf Landon for President (1936)  Literary Digest: post cards to voters in 6 states –Correctly predicting elections from Names selected from telephone directories and automobile registrationsNames selected from telephone directories and automobile registrations –In 1936, they sent out 10 million post cards Results pick Landon 57% to Roosevelt 43%Results pick Landon 57% to Roosevelt 43% –Election: Roosevelt in the largest landslide Roosevelt 61% of the vote and in Elect. Col.Roosevelt 61% of the vote and in Elect. Col.  Why so inaccurate?: Poor sampling frame –Leads to selection of wealthy respondents

Sampling Miscues: Thomas E. Dewey for President (1948)  Gallup picks winner –Use quota sampling: matches sample characteristics to population matches sample characteristics to population –Gallup quota samples on the basis of income  In 1948, Gallup picked Dewey to defeat Truman –Reasons: 1. Most pollsters quit polling in October1. Most pollsters quit polling in October 2. Undecided voters went for Truman2. Undecided voters went for Truman 3. Unrepresentative samples—WWII changed society since census3. Unrepresentative samples—WWII changed society since census

Non-probability Sampling  In situations where sampling frame for randomization doesn’t exist  Types of non-probability samples: –1. Reliance on available subjects convenience samplingconvenience sampling –2. Purposive or judgmental sampling –3. Snowball sampling –4. Quota sampling

Reliance on Available Subjects  Person on the street, easily accessible  Examples: –Mall intercepts, college students, e-polls  Frequently used, but usually biased  Notoriously inaccurate –Especially in making inferences about larger population, even with many respondents

Purposive or Judgmental Sampling  Dictated by the purpose of the study –Situational judgments about what individuals should be surveyed to make for a useful or representative sample E.g., Using college students to study third-person effects regarding rap and metal musicE.g., Using college students to study third-person effects regarding rap and metal music –3pe: Others are more affected by exposure than self Assessing effects on self and othersAssessing effects on self and others –Using college students makes for homogeneity of self

Snowball Sampling  Used when population of interest is difficult to locate –E.g., homeless people, meth addicts  Research collects data from of few people in the targeted group –Initially surveyed individuals asked to name other people to contact Good for explorationGood for exploration Bad for generalizabilityBad for generalizability

Quota Sampling  Begins with a table of relevant characteristics of the population –Proportions of Gender, Age, Education, Ethnicity from census data –Selecting a sample to match those proportions  Problems: –1. Quota frame must be accurate –2. Sample is not random, but can be representative

Probability Sampling  Goal: Representativeness –Sample resembles larger population  Random selection –Enhancing likelihood of representative sample –Each unit of the population has an equal chance of being selected into the sample

Population Parameters  Parameter: Summary statistic for the population –E.g., Mean age of the population  Sample allows parameter estimates –E.g., Mean age of the sample Used as an estimate of the population parameterUsed as an estimate of the population parameter

Sampling Error  Every time you draw a sample from the population, the parameter estimate will fluctuate slightly –E.g.: Sample 1: Mean age = 37.2Sample 1: Mean age = 37.2 Sample 2: Mean age = 36.4Sample 2: Mean age = 36.4 Sample 3: Mean age = 38.1Sample 3: Mean age = 38.1  If you draw lots of samples, you would get a normal curve of values

Normal Curve of Sample Estimates Frequency of estimated means from multiple samples Estimated Mean Likely population parameter

Error and Sample Size  As the sample size increases: –The error decreases –In other words, large sample estimate is likely to be closer to the population parameter –As the sample size increases, we get more confident in our parameter estimate

Confidence Interval  Interval width at which we are 95% confident the estimate contains the population parameter  For example, we predict that Candidate X will receive 45% of the vote with a 3% confidence interval –We are 95% sure the parameter will be between 42% and 48% –The “margin of error” in a poll  Confidence interval shrinks as: –Error is smaller –Sample size is larger

Sample Size & Confidence Interval  How precise does the estimate have to be? –More precise: larger sample size  Larger samples increase precision –But at a diminishing rate –Each unit you add to your sample contributes to the accuracy of your estimate But the amount it adds shrinks with additional unit addedBut the amount it adds shrinks with additional unit added

95% Confidence Intervals % split N = 100 N = 200 N = 300 N = 400 N = 500 N = 700 N = 1000 N = / / / Sample Size

Describe Sampling Frame  List of units from which sample is drawn –Defines your population –E.g., List of members of population  Ideally you’d like to list all members of your population as your sampling frame –Randomly select your sample from that list  Often impractical to list entire population

Sampling Frames for Surveys  Limitations of the telephone book: –Misses unlisted numbers/mobile numbers –SES and age bias: Poor people may not have phonePoor people may not have phone Less likely to have multiple phone linesLess likely to have multiple phone lines Young people have mobile phone numbersYoung people have mobile phone numbers  Most studies use a technique such as Random Digit Dialing as a way around this

Types of Sampling Designs  Simple Random Sampling  Systematic Sampling  Stratified Sampling  Multi-stage Cluster Sampling

Simple Random Sampling  Establish a sampling frame –A number is assigned to each element –Elements randomly selected into the sample –Use a random number generator to select every case you need for inclusion.

Systematic Sampling  Establish sampling frame –Select every k th element with random start –E.g., 1000 on the list, choosing every 5 th name yields a sample size of 200  Sampling interval: standard distance between units for the sampling frame –Sampling interval = pop. size / sample size  Sampling ratio: proportion of pop. selected –Sampling ratio = sample size / population size

Stratified Sampling  Modification used to reduce potential for sampling error –Research ensures that certain groups are represented proportionately in the sample E.g., If the population is 60% female, stratified sample selects 60% females into the sampleE.g., If the population is 60% female, stratified sample selects 60% females into the sample E.g., Stratifying by region of the country to make sure that each region is proportionately representedE.g., Stratifying by region of the country to make sure that each region is proportionately represented

Cluster Sampling  Frequently, there is no convenient way of listing the population for sampling –E.g., Sample of Dane County or Wisconsin Hard to get a list of the population membersHard to get a list of the population members  Cluster sample –Sample of census blocks List of census blocks, list people for selected blocksList of census blocks, list people for selected blocks Select sub-sample of people living on each blockSelect sub-sample of people living on each block

Multi-stage Cluster Sample  Cluster sampling done in a series of stages: –List, then sample within  Example: –Stage 1: Listing zip codes Randomly selecting zip codesRandomly selecting zip codes –Stage 2: List census blocks within selected zip codes Randomly select census blocksRandomly select census blocks –Stage 3: List households on selected census blocks Randomly select householdsRandomly select households –Stage 4: List residents of selected households Randomly select person to interviewRandomly select person to interview