ALC 208

WEEK 6- TOPIC 5

SAMPLING

Assigned Readings:

TEXT: Chapter 6 &

Reading 5.1: Purposive Sampling & Random Numbers Table (emailed)

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SAMPLING Pollsters got it wrong in the: - 2004 US and Australian Elections -2008 US Presidential Primaries in New

Hampshire Didn’t get it right either for the: -2008 & 2012 US Presidential elections as very

close -1936 Presidential Election by the Literary Digest

with 2 million respondents Variations in figures by different polling

companies are caused by issues related to sampling, questions posed to subjects and other factors

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SAMPLES & POPULATIONS

Not possible to study everyone all the time.

A Population- All relevant members

A census- Like an inventory of everyone /all

A sample- Used to make estimations of the population. ‘Clothing samples’, shoes displayed in a shop

A Representative sample- represents all subsets of the population

A Non-representative sample- Does not represent all subsets of population

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ERRORS IN THE RESEARCH PROCESS & FINDINGS

Measurement error- Errors in the instrument. E.g Survey, content analysis coding manual, interview questions posed to subjects. Can occur in any study.

Sampling error-How much the sample differs from the population eg. Asking 1st year students about a new major and not year 12 students. The Literary Digest only read by rich Republicans- not all voters

Random error- Resulting due to unknown, unexpected or uncontrolled factors. e.g. Falklands War helped Thatcher; Tampa helped John Howard

Margin of error- Given as + or – 3 or 4% -3___-2___-1__0__+1 __+2__+3 Gap is 6 -4___________________0________________+4 Gap is 8 If Gillard is 53% and Abbott is 45% in popularity with + or -4% margin, the gap is 8%. So unless the gap is higher than 8%, it is ‘neck and neck’

or ‘too close to call’

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TYPES OF SAMPLING

Probability Sampling- selected according to mathematical guidelines- Draw lots to select 10% of students in class; Tossing a coin.

Helps calculate Sampling error.

Non-probability sampling- Not selected according to mathematical guidelines. Cannot calculate sampling error, findings not generalisable to the population.

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WHEN TO USE PROBABILITY / NON-PROBABILITY (NP)

SAMPLING Depends on: 1. Purpose of study- NP for exploratory studies

2. Cost vs Value - budget and importance of project /data

3. Time constraints – NP when in a hurry and for preliminary information

4. Level of acceptable error- NP for pilot studies, if control of error is not important

Rule of Thumb: Use probability sampling when study is very important and results need to be generalised.

A probability sample does not guarantee representativeness. e.g randomly selected sample of 50 people for a TV study may draw 50 subjects with no TV at home.

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TYPES OF NON-PROBABILITY SAMPLING

Convenience or available sample- readily available subjects. e.g. Students in a class, mall shoppers

-Suitable for exploratory studies, to check instrument etc. Findings not generalisable.

-Involves researcher subjectivity and sampling bias e.g. Choose friendly looking people

- Choosing every 10th person passing the researcher at the mall etc. does not make it random

-Law of Large Numbers- A very large sample also does not make it a probability sample. 7

VOLUNTEER SAMPLES -Anyone willing to participate when invited. E.g. SETUs;

telephone /Internet voting for reality TV- Big Brother; mail surveys

- Sample not representative as volunteers are very different from others. They are more educated, high status, more intelligent, altruistic, less authoritarian (not bossy) etc.

-May lead to supporting researcher’s hypothesis and skewed results.

-Radio/TV/Internet polls only available to those with the technology and if tuned in at the time. e.g. Literary Digest poll only available to its readers in 1936

-Some claim all research subjects are ‘volunteers’ due to ethics requirement of ‘voluntary participation’.

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PURPOSIVE SAMPLES

-Sample ‘fits the purpose’ of study.

-Eliminates those not fitting criteria e.g. Casting directors of reality TV shows use

the method (See Reading 5.1)

-Often used in advertising e.g. Wearers of eye glasses

- Method used by journalists when interviewing people for a news story.

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QUOTA SAMPLING

-Similar to purposive sampling but uses known and predetermined percentages from various groups in the population. e.g. 50% men & 50% women.

Snowball Sampling (referrals) -Common in academic research

-Contact one or few suitable subjects and ask them to recommend others

- May only find people of same circle, network etc and not a diverse group.

-Best when seeking subjects with rare characteristics. Eg. Ask Akmal Saleh when seeking other Muslim comedians.

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TYPES OF PROBABILITY SAMPLING

Random Sampling- Each subject has equal chance of being included. e.g. Raffle

Random Sampling without replacement- When the first subject drawn is NOT put back into the draw before the next is drawn. Probability may reduce as selection goes on. E.g. 1/1000. 1/999, 1/998 etc. Most often used system.

Random Sampling with replacement- When the first subject drawn is put back into the draw before the next is drawn. Helps maintain probability the same throughout. e.g.1/1000. Used in nationwide surveys. 11

RANDOM NUMBERS TABLES Tells us the order for using random numbers.

can be generated using http://www.randomizer.org.

Indicate size of population and size of sample.

Random digit dialling Used in telephone surveys to call people selected

at random.

Use computer generated random digits for the last four numbers, select the suburb prefix, then the state prefix. Eg. (03) 271 1234 etc

- But this includes silent, non-existent and discontinued numbers. Need three times the numbers needed to call.

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SAMPLING FRAME

The complete list of members of the population used when selecting a sample. E.g. Deakin internal directory of staff; Callista database that lists all students.

A Telephone directory is incomplete as it does

not include silent numbers or list everyone in a home with a landline or include those without landlines.

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SYSTEMATIC RANDOM SAMPLING

A system where every nth subject or unit is selected from a population (N) for the sample (S).

Calculate the sampling interval n= N/S

Randomly select the starting point (e.g. 8) and select the nth subject thereafter from the sampling frame.

e.g. If n=6, the subjects selected will be 8; (8+6)= 14; (14+6)= 20; (20+6)= 26 etc. till you have your 20 subject sample.

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CYCLICITY A systematic random sample can end up ‘biased’

due to cyclicity if it involves a ‘cycle’ such as days of the week, months of the year etc.

e.g. If n= 7, we end up choosing newspapers of the same day of the week for the period under study; If n=12, we could choose magazines of the same month for the number of years of the study.

Therefore researchers use a composite week or year to select the sample from the period.

e.g. randomly choose a day of the week (say Thursday) and choose Thursday from week1, Friday from week 2 etc.

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PERIODICITY The way subjects are listed in the sampling

frame can bias the selection of the sample, when using systematic random sampling.

e.g. In selecting a sample of movies for 2008 from the sampling frame, we could end up selecting films of the same genre or the same studio. This results in an unrepresentative sample.

e.g. In an alphabetical listing of names, common last names (say Smith) will have a better chance of being included as the sampling interval (n) can jump over the uncommon ones. 16

STRATIFIED (STRATA OR SEGMENT) SAMPLING

Guarantees that sufficient numbers of subjects from specific sub-samples in the population are represented in the sample, better than random sampling can and reduces sampling error.

These strata can be based on sex, age, education, income etc.

e.g. Income groups of: below $35,000/year; $36-$60,000’ $61-$80,000; $81,000 or above

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PROPORTIONATE STRATIFIED SAMPLING

Various groups in the sample are selected based on their proportion in the population.

E.g. If census figures indicate that 20% of the people earns less than $36,000/year, the sample will have the same percentage of subjects from that income group.

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DISPROPORTIONATE STRATIFIED SAMPLING

Over sample or over represent a particular group in the sample if that stratum is important to the project.

E.g. Households earning over $100,000 to over represent a project related to luxury cars, as they are the ones who can afford the product.

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TYPES OF SAMPLING USED IN QUALITATIVE RESEARCH Uses non-probability sampling and small

samples Findings not generalisable to the population Provides useful and even unique insights and

opinions of subjects Generally uses purposive, snowball or

convenience samples Type of subject, sampling method and sample

size not finalised at the beginning Data collection ends when saturation point is

reached (no new info with additional interviews) Quantitative research decides on sampling

method, type of subject and sample size before data collection begins 20

CHOOSING SUITABLE SAMPLE SIZE Optimal sample size- (The best under the

circumstances) is the ‘smallest one possible and largest one necessary’

No standard sample size recommended for specific research ,method or area of study’

But some statistical analyses may require a minimum sample size. e.g. 300 surveys for a factor analysis; 6-12 for a focus group etc.

In a stratified sample, 50-100 sample size needed for each subset or strata e.g. Sex- 50 males/females each.

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SAMPLE SIZE DEPENDS ON: Type of project (method used) Purpose, complexity and amount of error that can be

tolerated (low in medical research) Time and financial constraints Previous research in area Size of budget (for private companies) Sample size of 10-50 sufficient for pilot studies, pre-

tests and for heuristic value Recommended sample sizes by population (size)

available, but different numbers given by different authors Margin of sampling error significantly reduced by a

larger sample only up to point, thereafter not proportionate with increased sample size

TV ratings research in USA (with over 300 million population) only uses a 3000-sized sample.

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Any Questions?

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