1. Week 9 Sampling a population
Research Methods
Week 9
Sampling a population

2. Review of Week 8 Hypotheses have specific qualities
Hypotheses should connect to some theoretical background
Hypotheses have two types
Hypotheses present two types of errors

3. Proving an hypothesis Test Data is derived from – A population
A sample drawn from the population

4. Populations
If we test a population, we are gathering data from every instance in that population.
Examples –
Every sale made at our store
Every customer in our database
Every student in our country
Every driver in our country
Since such populations are large, in the past we used samples (parts of the whole population).

5. Populations
Now computers have sufficient power to examine entire populations in real time. (Big data).
Google reviews all text messages in real time – “trending”
News organizations monitor all searches. What are people most curious about? Justin Bieber
Google monitors all s for key words – products you might be interested in.
If we can gather data from an entire population, we no longer need to sample.

6. So why sample? We aren’t Google
We may want to treat less than the whole population (cost, time, risk)

7. Samples
If we choose to take a portion of a population for examination, we have several choices:
geographic - such as state, district, village
social unit - such as family, club, school, classroom, work group
Demographic – age, ethnicity, income
Psychographic – personality types (e.g. aspirant consumers)

8. Sampling frame Source list: (where we will pull our sample from)
It contains the names of all items of a universe. If a source list is not available, the researcher has to prepare it. Such a list should be comprehensive, correct, reliable and appropriate. It is extremely important for the source list to be as representative of the population as possible.
Examples – every student in a school, every adult in a community, every business owner in Oman

9. Sampling frame Source list: (where we will pull our sample from)
Qualities – who are these people? Age, gender, education, experience.
Can you describe these people?
“I distributed the survey to people who had completed an on-line class”
“I distributed the survey to people who had completed an ERP project lasting over 4 months.”

10. Sampling frame Can you describe these people?
“I distributed the survey to people who had completed an on-line class”
You will need to describe your sample in the research report. It will be important for generalizations and discussion later.
Were all students female?
Were many new to on-line education, or had they taken more than one course?
Were they all good students? Average?
If the on-line course was in English, did they all have good English skills?

11. Parameters of interest
We may adjust the sample to ensure we get enough of the population with some characteristic, or some sub-group of the population. For example, we may want to get a sufficient number of women in our sample, or be careful to include people from all major age groups.

12. Parameters of interest
What would we want to check for these populations:
Students taking an on-line course?
Workers doing HR record updates?
People on a ERP project team?
People who have completed an ERP project?

13. Sample Size
The size of sample should ensure efficiency, representativeness, reliability. The population variance needs to be considered. If variance is high, a larger sample will be needed to ensure representativeness.
How would you know in advance that variance is likely to be large?

14. Sample Size
Very small samples become much harder to evaluate statistically. Very large samples may show “statistically significant” differences that are not practically significant.
Good size? – per group of interest

15. Common Problems
1. Inappropriate sampling frame: If the sampling frame is inappropriate i.e., a biased representation of the universe, it will result in a systematic bias. (We only surveyed project teams that had undergone extensive training.)
2. Defective measuring device (survey or person): Bias can result if the questionnaire or the interviewer is biased (We ask leading questions – “Why do you think project training is important?”).
3. Non-respondents: If we are unable to sample all the individuals initially included in the sample, there may arise a systematic bias. (Lower level employees were reluctant to participate)
4. Indeterminancy principle: Individuals may act differently when kept under observation than when in non-observed situations. They may fill in the survey giving you the answers they think you want.
5. Natural bias in the reporting of data: People in general understate their incomes if asked about it for tax purposes, but they overstate their income if asked for social status or their affluence. They may overstate the hours they studied.

16. Characteristics of good sampling design
Sample design must result in a truly representative sample.
Sample design must result in a small sampling error.
Sample design must be affordable.
Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence. (As you consider the sample, already be considering how you will explain the result.)

17. Good sampling design – how would I know?
Sample design must result in a truly representative sample. Is a work group representative? Is this class room typical?
Sample design must result in a small sampling error. Can I judge the variance in the group?

18. Non probability (non random)
Sampling Types
Non probability (non random)
Researchers purposely choose the sample based on their judgment that the group they select will be typical or representative of the whole.
“It will play in Peoria” (A small city in the middle of the US thought to represent most American small cities)
BUT – the chance of selection bias is real. The selection may be based on outdated information. (Peoria may no longer be typical. This university may not be typical. These workers may not be typical.)
So, this sampling design is rarely adopted in inquires of importance.

19. Non probability (non random)
Sampling Types
Non probability (non random)
Researchers purposely choose the sample based on their judgment that the group they select will be typical or representative of the whole.
How would you justify picking a group? You pick one group of outsource workers. How would you explain they are representative?

20. Probabilistic Sampling
Systematic sampling: Select every ith item on a list. Use a random number to pick the first one, and then pick every 10th (or 25th) item, depending upon the sample size desired. Example – you get test results for every student at a school, then review the results of 30, starting from the 5th person on the attendance form.

21. Probabilistic Sampling
Stratified sampling
If a population from which a sample is to be drawn is not a homogeneous group (e.g. clustered by ethnicity or gender), stratified sampling divides the population into several sub-populations. Then we randomly select items from each stratum to constitute a sample.
We use proportional allocation -- the sizes of the samples from the different strata are kept proportional to the sizes of the strata. (e.g. you draw from 3 high schools with sizes of 400, 200, and 600 students. Pick 3 times as many students from the largest school.)

22. Probabilistic Sampling
Cluster sampling
Divide the population into a number of smaller non-overlapping areas and then to randomly select a number of these smaller areas. Then randomly select units in these small areas.
Example – randomly select 5 ministries, and then randomly select a sample of workers from each ministry. This is also called “area” sampling”.

23. Probabilistic Sampling
Multi-stage sampling
Use the cluster idea to narrow the sample again and again through stages –
Example – we are interested in the health of older people. – select certain countries, then certain cities, then certain neighborhoods, then pick a sample of older people.

24. Probabilistic Sampling
Sampling with probability proportional to size
Use the cluster idea to narrow the sample again and again through stages – but – adjust the size of the final sample to the size of the cluster.
Example – We select certain countries, then certain cities. Since the cities and countries will be of different size, we pick a sample size proportional to the country and city size.

25. Application Pick the best sampling technique for –
Systemic sampling
Stratified sampling
Cluster sampling
Multi-stage sampling
Sampling proportionate to size
Pick the best sampling technique for –
Speed of HR record updating
ERP project success
IS project team success
Student learning on-line