Chapter 6 Flashcards
How do you sample in a quantitative study? What techniques do you use? Define those techniques.
To create representative samples in quantitative research you need to use very precise sampling procedures. These procedures rely on the mathematics of probabilities, hence they are called probability sampling. Probability samples can be highly accurate. To summarise, you sample a small set of cases that is a mathematically accurate reproduction of the entire population so you can make statements about categories in the population.
How do you sample in a qualitative study? What techniques do you use? Define those techniques.
The logic of the qualitative sample is to sample aspects of the social world that will highlight key dimensions in a complex social life. Instead of probability sampling, qualitative researchers use nonprobability or nonrandom samples. This means they rarely determine the sample size in advance and have limited knowledge about the population from which the sample is taken.
What are the techniques of nonprobability sampling?
Convenience, quota, purposive (or judgemental), snowball, deviant case and sequential.
What is the convenience sampling technique?
In convenience sampling your primary criteria for selecting cases is that they are easy to reach. Unfortunately, it often produces very nonrepresentative samples. It is not recommended if you want to create an accurate sample to represent the population.
What is the quota sampling technique?
a well-designed quota sample is an acceptable nonprobability substitute method for producing a quasi-representative sample. In quota sampling, you first identify relevant categories among the population you are sampling to capture diversity among units. Next, you determine how many cases to get for each category—this is the “quota”. Thus, you fix a number of cases in various categories of the sample at the start. This technique ensures some variety in the sample.
What is the purposive (or judgemental) sampling technique?
Purposive sampling is a valuable kind of sampling for special situations. In this type of sampling the judgement of an expert or prior knowledge is used to select cases. It is most appropriate if one wishes to select unique cases that are especially informative, if one wishes to select members of a difficult to reach population or if one wants to identify particular types of cases for in-depth investigation.
Sampling goes on until resources (time, money, energy) are drained.
What is the snowball sampling technique?
Snowball sampling is a method for identifying and sampling (or selecting) the cases in a network. Snowball sampling is a multistage technique. You begin with one or a few people or cases and spread out on the basis of links to the initial cases. An important use of snowball sampling is to sample a network. This is a chain referral sampling method and it is based on spreading the world.
The respondent driven sampling technique is similar.
What is the deviant case sampling technique?
You might use deviant case sampling when you seek cases that differ from the dominant pattern. You select the deviant cases because they are different, and you hope to learn more about the social life by considering cases that fall outside the general pattern or including what is beyond the main flow of events.
What is the sequential sampling technique?
One gathers observations which are later tested to see whether or not the null hypothesis can be rejected. If the null is not rejected, then another observation or group of observations is sampled and the test is run again.
In sequential sampling, you continue to gather cases until the amount of new information or diversity of cases is filled.
What is a sampling element?
A sampling element is the unit of analysis or case in a population.
What is the population?
The large pool is the population, which has an important role in sampling. To define the population, you specify the unit being sampled, the geographical location, and the temporal boundaries of populations.
What is the target population?
The term target population refers to the specific pool of cases that you want to study.
What is the sampling ratio?
The ratio of the size of the sample to the size of the target population is the sampling ratio. The population needs to be estimated and it needs an operational definition as it is an abstract concept.
What is the sampling frame?
A sampling frame represents a list with approximately all the elements in the population (developed by operationalising the population). It represents a narrowed down area of observation.
What is a parameter?
Any characteristic of a population is a population parameter. It is the true characteristic of the population. Parameters are determined when all elements in a population are measured. The parameter is never known with absolute accuracy for large populations. This is why they need to approximate them. They use information from the sample, called a statistic, to estimate population parameters. A sampling distribution indicates the mean scores.
What is randomisation?
Randomisation of sampling is highly important as it represents a selection process without any patterns.
What is a sampling error? What are its causes?
A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data. As a result, the results found in the sample do not represent the results that would be obtained from the entire population. Can be cause by randomness due to not studying the entire population and/or an unrepresentative sample (sampling bias).
What are the techniques of probability sampling?
Simple random, systematic, stratified, cluster (PPS & RDD)
What is the simple random sampling technique?
After developing a sampling frame, one must select the elements from it, according to mathematical random procedures. After numbering all of the elements, using a list of numbers, you select some cases to analyse.
What is the systematic sampling technique?
Again, your first step is to number each element in the sampling frame. Instead of using a list of random numbers, you calculate a sampling interval. The interval becomes your quasi-random selection method. To use the interval, you should not always start at the beginning, but use a random process to pick a starting point, then use the interval. Continue to the beginning as if all numbered elements were in a circle. Stop when you return to your random starting point.
What is the stratified sampling technique?
In stratified sampling, you first divide the population into subpopulations (strata) on the basis of supplementary information. After dividing the population into strata, you draw a random sample from each subpopulation. In stratified sampling, you control the relative size of each stratum, rather than letting random processes control it. The main situation in which we use stratified sampling is when a stratum of interest is a small percentage of a population and random processes could miss the stratum by chance. In special situations, you may want the proportion of a stratum in a sample to differ from its true proportion in the population. With this type of disproportionate stratified sample, you cannot generalise directly from the sample to the population without special adjustments.
What is the cluster sampling technique?
A cluster is a unit that contains final sampling elements but you can treat it temporarily as a sampling element itself. Here is how cluster sampling works: you first sample clusters, each of which contains elements, and then you draw a second sample from within the clusters selected in the first stage of sampling. Each stage in cluster sampling can introduce sampling errors. Furthermore, if you use cluster sampling, you must decide the number of clusters and the number of elements within each cluster. Overall, a design with more clusters is better. This is because elements within clusters tend to be similar to each other.
What is the probability proportionate to size method?
Sampling is proportionate because the size of each cluster (or number of elements at each stage) is the same. The more common situation is for the cluster groups to be of different sizes. When this is the case, you must adjust the probability or sampling ratio at various stages in sampling. It refers to the probability of an individual to be selected from the larger pool, according to the size of the cluster. Hence, you assure randomisation.
What is the random digit dialling method?
Random-digit dialling (RDD) is a sampling technique used in studies in which you contact the public by telephone. The population is telephone numbers, not people with telephones. Random-digit dialling is not difficult, but it takes time and can frustrate the person doing the calling.
What is the respondent driven sampling technique? What is it used for?
Sampling hidden populations is a recurrent issue in the studies of deviant or stigmatised behaviour. RDS shows ingenuity of both probability and non-probability sampling.
Respondent driven sampling (RDS) is a version of snowball sampling and is appropriate when members of a hidden population are likely to maintain contact with one another. It begins by identifying an eligible case or participant. Researchers give this person, called a “seed,” referral coupons to distribute among other eligible people who engage in the same activity. Referrals are limited to a number of 3 so that the sample is not biased. For each successful referral, the “seed” receives money. This process is repeated with several waves of new recruits until a point of saturation. It is important that this study keeps track of who contacted who and what the relationships between people are.
How can you decide the size of a sample?
The size of a sample depends on the kind of data analysis the researcher plans, on how accurate the sample has to be for the purposes, and on population characteristics. A large sample size alone does not guarantee a representative sample. A large sample without random sampling or with a poor sampling frame is less representative than a smaller one with random sampling and an excellent sampling frame.
What is the law of large numbers?
The law of large numbers is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed.
