To take the database example one stage further, it might mean dividing the database into geographic regions and then sampling within each region in order to ensure the sample matches the known profile of the database. In the database cases, the sample is drawn from a known list. But in most research cases there is no list to draw from - there is no known list of internet users, or mobile phone owners, or owners of a particular car, or drinkers of a particular beer.
In these cases sampling moves from the theoretical purity of a 1 in N sample to something which balances purity of design with practicality of locating the people you wish to interview. With no list to work with, if you really require a random sample eg for Government statistics or measuring media use where people pay for a particular level of advertising exposure telephone used to be one of the best methods. For random telephone samples the broad principle is that you set a computer to call numbers at random in order to make contact with individuals.
In practice pure random numbers isn't so efficient, but phone companies often allocate numbers in blocks and in places like the US, databases of these blocks existed.
It was then a process of randomly selecting a block and then selecting a number within the block at random. And then, if it's a household number, selecting an individual in the household at random.
This was at least the principle before mobile phones and before homes with multiple phone lines. It is still used as a method, but once you have more than one line, or a line that might be turned on or off according to the weight of use, the quality of the randomness starts to diminish - albeit slightly compared to other sampling methods.
Essentially people with more than one line, are more likely to be called. If you have a mobile phone on more of the time, you are more likely to be called. Mobiles also cause problems because mobile numbers are used differently than fixed lines - you have no idea where the person receiving the call might be.
They could be overseas, in which case they might get charged to receive your call. They could be driving in which case you shouldn't be interviewing them. If you're still looking for a random sample and telephone is not applicable, face-to-face might be an option.
It's not common in the United States because of the geography and distances involved, but it was common in the UK for major surveys like follow ups to the government census or major health studies. In a face-to-face random survey you again need a list. In the UK this was the electoral roll.
You would pick individuals at random from the electoral role, then go and interview them, returning several times if they were out. Though very pure statistically, these type of survey are extremely labour intensive and so extremely expensive. For this reason alternatives were developed. The main ones split the country into geographic regions, sometimes down to blocks of houses enumeration districts that are used to ensure full coverage for the census. Then starting with a list of houses or small geographic areas, you would pick areas at random, then allocate interviewer to visit the households in those areas.
You would control for who was at home eg employees were likely to be out during the day by controlling the time of the interviews and who could be interviewed. This is still expensive, but at least manageable in terms of allocating an interviewing team and is still the dominant method for conducting face-to-face omnibus studies and media studies where randomness is important in order to properly measure survey-to-survey variations.
What about online research? In practice, unless you have your own database list it's very difficult to pull a true random sample. And any sample drawn from a database is really only representative of the database. However, practice eg yougov opinion polls and the current size of the panels seems to suggest that if you have a large proportion of the population on your lists you can draw a sample and get results like a random sample. In the main, unless you have detailed list to start with or can use random-digit dialling, fully randomised samples as expensive and difficult to obtain.
For most categories - eg recent car buyers, users of fly spray, visitors to Bristol - a list simply isn't available. For some of these types of categories you can do a 'screen'. In a screen you take a random or pseudo-random sample and then use a screener questionnaire also known as a recruitment questionnaire to identify the core group you wish to interview.
For groups that are a small part of a larger population this can mean asking thousands of people to help to get just a few hundred responses. So instead, researchers will use what are known as 'convenience' samples. A convenience sample means finding people who fit the criteria, but not worrying about whether the sample is genuinely random. An example is stopping people in the street to ask them to take part in a survey street interviewing.
Here you can only interview people who are passing so you do not have a genuine random sample - for instance it's likely to be biased towards people not working, able-bodied people and often younger females during the day. Similarly, an online panel is, in reality, another form of convenience sample. The people who sign up for an online panel are not necessarily representative of the full range of views in the market because you don't know if there is a bias introduced by getting people to sign up eg if you ran a survey on privacy, you might find panel respondents less concerned about privacy than those who have not signed up to a panel.
A classic convenience sample is a company's own customer lists. This introduces a natural bias towards the company and the company's products - it will not include many non-customers or people who reject the company's products. This can be acceptable within known limits, but it is something to be very careful of. This hidden type of bias comes into a lot of database and web-analytics as these internal sources of information can only provide information about the people who bought, or who visited and not those who didn't.
With the consequence that it can be very difficult to say anything about why people don't become customers or don't spend a long time on the website. Because of the hidden potentials for bias in convenience sampling, one method for control is to set quotas to ensure that a certain number of interviews are achieved in certain categories.
This might include setting quotas by age, or working status, or socio-economic grade, but in business-to-business surveys might include the sector the companies that do the most marketing are typically the least likely to do market research surveys - local government the most likely to take part , or size of the business. A quota is then used to set a target and a limit on the number of interviews to be achieved. If the quotas are set very tightly it can make it very difficult to find the last few interviews, but too loose and the sample will tend towards the easy to find categories of respondents.
Adding quotas and setting interview targets, doesn't make the sample random but for reasons such as cost or speed it may be considered the best available sample for the job. Obviously the researcher needs to keep an eye on potential biases, but there is one more hidden potential bias, even with random samples. A senior living community developer is looking to institute new activities and opportunities for the members of their communities. They are quite confident that those in the older age ranges will react differently than those in the younger age ranges.
By randomly surveying each stratum, it ensures that there are enough respondents from each age group to make meaningful comparisons.
The population is divided into mutually exclusive clusters. This method relies on assumption that each group is quite similar. Certain clusters are then randomly selected and the people within the cluster are surveyed.
A school district is hoping to conduct qualitative research with the community to learn more about impressions of programs they are looking to institute. They separate the district into 30 major neighborhoods. By doing this, the sample still remains randomized, while cutting administrative, logistical and travel costs.
These methods allow the market research to confidently state the results are representative of the population to a certain margin of error, of course. Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed. Want to stay on top of the latest market research trends?
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Area sampling is basically multistage sampling in which maps, rather than lists or registers, serve as the sampling frame. This is the main method of sampling in developing countries where .
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