# NON-PROBABILITY SAMPLING TECHNIQUES

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1 NON-PROBABILITY SAMPLING TECHNIQUES PRESENTED BY Name: WINNIE MUGERA Reg No: L50/62004/2013 RESEARCH METHODS LDP 603 UNIVERSITY OF NAIROBI Date: APRIL 2013

2 SAMPLING Sampling is the use of a subset of the population to represent the whole population. NON-PROBABILITY SAMPLING Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances (equal probability) of being selected. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher. The downside of this is that an unknown proportion of the entire population may not be sampled. This entails that the sample may or may not represent the entire population accurately. Therefore, the results of the research cannot be used in generalizations pertaining to the entire population. Despite various limitations and criticisms, the numerous advantages of non-probability methods include: 1. They are cheaper 2. They are used when a sampling frame is not available 3. They are useful when population is so widely dispersed that cluster sampling would not be efficient 4. They are often used in exploratory studies, e.g. for hypothesis generation 5. They are used on research that is not interested in working out what proportion of population gives a particular response but rather in obtaining an idea of the range of responses on ideas that people have. TYPES OF NON-PROBABILITY SAMPLING TECHNIQUE The various types of non-probability sampling technique include: 1. Convenience/ Haphazard /Accidental sampling This is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. 1

3 Example: In a manufacturing plant with 500 employees, we were only interested in achieving a sample size of 100 employees who would take part in our research. As such, we would continue to invite employees to take part in the research until our sample size was reached. Since the aim of convenience sampling is easy access, we may simply choose to stand at one of the main entrances of the manufacturing plant where it would be easy to invite the many employees that pass by to take part in the research. Advantages of convenience sampling Many researchers prefer this sampling technique because: Convenience sampling is very easy to carry out with few rules governing how the sample should be collected. The relative cost and time required to carry out a convenience sample are small in comparison to probability sampling techniques. This enables you to achieve the sample size you want in a relatively fast and inexpensive way. The convenience sample may help you gathering useful data and information that would not have been possible using probability sampling techniques, which require more formal access to lists of populations because the subject are readily available. Disadvantages of convenience sampling The convenience sample often suffers from biases from a number of biases. o Considering the above example, a convenience sample can lead to the underrepresentation or over-representation of particular groups within the sample. Maybe the organization has multiple sites, with employee satisfaction varying considerably between these sites. By conducting the survey at the headquarters of the organization, we may have missed the differences in employee satisfaction amongst non-office workers. Since the sampling frame is not known, and the sample is not chosen at random, the inherent bias in convenience sampling means that the sample is unlikely to be representative of the population being studied. This undermines your ability to make generalizations from your sample to the population you are studying. 2

4 2. Consecutive sampling This is very similar to convenience sampling except that it seeks to include ALL accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that makes the sample a better representation of the entire population. 3. Snowball sampling Some populations that we are interested in studying can be hard-to-reach or are hidden because they exhibit some kind of social stigma, illicit or illegal behaviours, or other traits that makes them socially marginalized. These include populations such as drug addicts, homeless people, individuals with AIDS/HIV and prostitutes. Snowball sampling is a non-probability based sampling technique that can be used to gain access to such populations. The researcher therefore asks the initial subject to identify another potential subject who also meets the criteria of the research. Example: If a researcher is interested in the gay community, he/she will due to the sensitivity of the study, ask the initial gay participant who agreed to take part in the research to help identify other gay participants that may be willing to take part. For ethical reasons, these new research participants should come forward themselves rather than being identified by the initial participant. In this respect, the initial participants help to identify additional units that will make up our sample. The process continues until sufficient units have been identified to meet the desired sample size. Advantages of snowball sampling Snowball sampling is useful because: There are no lists for e.g. drug users, Prostitutes that a researcher could get access to. It can therefore be difficult to identify units to include in the sample. The sensitivity of coming forward to take part in research is more acute therefore individuals that are likely to be less willing to identify themselves and take part in a piece of research than many other social groups. However, since snowball sampling involves individuals recruiting other individuals to take part in a piece of research, there may be common characteristics, traits and other social factors between those 3

5 individuals that help to break down some of the natural barriers that prevent such individuals from taking part. The unknown and secretive nature of some social groups may also make it difficult to identify strata that warrant investigation. In the case of drug users, it may be obvious to identify strata such as gender (i.e., male or female), types of drug user (e.g., causal, addict), and so forth, but others may be unknown to the researcher. The snowball sample may be helpful in exploring potentially unknown characteristics that are of interest before settling on your sampling criteria. There may be no other way of accessing your sample therefore snowball sampling is the only viable choice of sampling strategy. Disadvantages of snowball sampling It is impossible to determine the possible sampling error and make statistical inferences from the sample to the population since snowball sampling does not select units for inclusion in the sample based on random selection. As such, snowball samples should not be considered to be representative of the population being studied. 4. Judgmental sampling or Purposive sampling The researcher chooses subjects with a specific goal of focusing on particular characteristics of a population that are of interest, which will best enable him/her to answer the research questions. This is used primarily when there is a limited number of people that have expertise in the area being researched. These purposive sampling techniques include: Maximum variation sampling Homogeneous sampling Typical case sampling Extreme (or deviant) case sampling Total population sampling Expert sampling 4

6 Maximum variation sampling/ heterogeneous sampling Maximum variation sampling is used to capture a wide range of perspectives relating to the thing that you are interested in studying. The basic principle behind maximum variation sampling is to gain greater insights into a phenomenon by looking at it from all angles. This can often help the researcher to identify common themes that are evident across the sample. Homogeneous sampling Homogeneous sampling aims to achieve a sample whose units share the same characteristics. In this respect, homogeneous sampling is the opposite of maximum variation sampling. A homogeneous sample is often chosen when the research question that is being addressed is specific to the characteristics of the particular group of interest, which is subsequently examined in detail. Typical case sampling This purposive sampling technique is used when the units (people, cases, events,) you are interested in are typical. The word typical here means that the researcher has the ability to compare the findings from a study using typical case sampling with other similar samples. Therefore, with typical case sampling, you cannot use the sample to make generalizations to a population, but the sample could be illustrative of other similar samples. Whilst typical case sampling can be used exclusively, it may also follow another type of purposive sampling technique, such as maximum variation sampling, which can help to act as an exploratory sampling strategy to identify the typical cases that are subsequently selected. Extreme (or deviant) case sampling Extreme case sampling is used to focus on cases that are special or unusual. These extreme cases are useful because they often provide significant insight into a particular phenomenon, which can act as lessons that guide future research and practice. 5

7 Critical case sampling Critical case sampling is frequently used in exploratory, qualitative research, research with limited resources, as well as research where a single case (or small number of cases) to assess whether the phenomenon of interest even exists (amongst other reasons). Total population sampling Total population sampling is where you choose to examine the entire population that have a particular set of characteristics. In such cases, the entire population is often chosen because the size of the population that has the particular set of characteristics that you are interested in is very small. Expert sampling Expert sampling is used when your research needs to glean knowledge from individuals that have particular expertise. Expert sampling is particularly useful where there is a lack of empirical evidence in an area and high levels of uncertainty, as well as situations where it may take a long period of time before the findings from research can be uncovered. Therefore, expert sampling is a cornerstone of a research design. Advantages of purposive sampling Purposive sampling has a wide range of sampling techniques that can be used to achieve the goals of the wide range of qualitative research designs that researchers use. Whilst the various purposive sampling techniques each have different goals, they can provide researchers with the justification to make generalizations from the sample that is being studied, whether such generalizations are theoretical, analytic and/or logical in nature. Qualitative research designs can involve multiple phases, with each phase building on the previous one. In such instances, different types of sampling technique may be required at each phase. Purposive sampling is useful in these instances because it provides a wide range of non-probability sampling techniques for the researcher to draw on. For example, critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting an expert sampling approach to examine specific issues further. 6

8 Disadvantages of purposive sampling Purposive samples, irrespective of the type of purposive sampling used, can be highly prone to researcher bias. The idea that a purposive sample has been created based on the judgment of the researcher is not a good defense when it comes to alleviating possible researcher biases. However, this judgmental, subjective component of purpose sampling is only a major disadvantage when such judgments are ill-conceived or poorly considered. The subjectivity and non-probability based nature of unit selection selecting people, cases, etc.) in purposive sampling means that, it can be difficult to convince the reader that the judgment used to select units to study was appropriate. For this reason, it can also be difficult to convince the reader that research using purposive sampling achieved theoretical/analytic/logical generalization. After all, if different units had been selected, would the results and any generalizations have been the same? 5. Quota sampling In this sampling technique the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota. Uses: Quota sampling is often used in market research because it does not require a list of potential respondents (a 'sampling frame'). It is not based on random selection. Instead, respondents who fit into predetermined categories ('quota controls') are found by interviewers until their quotas are filled. Quota sampling is used when the distribution of target population is known across a set of groups and the researcher wants to have a distribution of the sample as per the population distribution. It is also used when one wants to ensure that minorities are properly represented in the study. 7

9 Example Imagine we were interested in comparing the difference in levels of job satisfaction between male and female employees in a manufacturing plant with 500 employees. We would then want to ensure that the sample we selected had a proportional number of male and female employees relative to the population. Therefore, the total number of male and female employees included in our quota would only be equal if 250 employees were male and the other 250 were female. Since this is unlikely to be the case, the number of units that should be selected for each stratum will vary. If there are 300 male employees (60% of the total) and 200 female employees (40% of the total), our sample would need to be made up of 60% males and 40% females. If our sample size is 100 employees, then 60 males and 40 females would be included. Once you have selected the number of cases you need in each stratum, you simply need to keep inviting participants to take part in your research until each of these quotas are filled. Advantages of quota sampling Quota sampling is particularly useful when you are unable to obtain a probability sample, but you are still trying to create a sample that is as representative as possible of the population being studied Quota sampling is much quicker and easier to carry out because it does not require a sampling frame and the strict use of random sampling techniques. The quota sample improves the representation of particular strata (groups) within the population, as well as ensuring that these strata are not over-represented. The use of a quota sample, which leads to the stratification of a sample (e.g., male and female employees), allows us to more easily compare these groups (strata). Disadvantages of quota sampling In quota sampling, the sample has not been chosen using random selection, which makes it impossible to determine the possible sampling error. Indeed, it is possible that the selection of units to be included in the sample will be based on ease of access and cost considerations, resulting in sampling bias. It also means that it is not possible to make statistical inferences from the sample to the population. This can lead to problems of generalization. 8

10 It must be possible to clearly divide the population into strata; that is, each unit from the population must only belong to one stratum. In the above example, this would be fairly simple, since our strata are male and female employees. o But extending the sampling requirements such that we were also interested in how their job satisfaction changed depending on their age groups, complicates the process as well as increases overall sample size required for the research, which can increase costs and time to carry out the research. 6. Self-selection sampling Self-selection sampling is useful when we want to allow units, whether individuals or organizations to choose to take part in research on their own accord. They are not approached by the researcher directly. There may be a wide range of reasons why people volunteer for such studies, including having particularly strong feelings or opinions about the research, a specific interest in the study or its findings, or simply wanting to help out a researcher. Examples: Researchers may put a questionnaire online and subsequently invite anyone within a particular organization to take part. Scientists that conduct experiments using human subjects may advertise the need for volunteers to take part in drug trials or research on physical activity. Advantages of self-selection sampling Since the potential research subjects contact you: This can reduce the amount of time necessary to search for appropriate individuals. The potential individuals are likely to be committed to take part in the study, which can help in improving attendance (where necessary), and greater willingness to provide more insight into the phenomenon being studied. 9

11 Disadvantages of self-selection sampling Since the potential research subjects volunteer to take part in the survey: There is likely to be a degree of self-selection bias. For example, the decision to participate in the study may reflect some inherent bias in the characteristics of the participants (e.g., an employee with a 'chip of his shoulder' wanting to give an opinion). This can either lead to the sample not being representative of the population being studied, or exaggerating some particular finding from the study. RESEARCH APPROACHES IN WHICH NON- PROBABILITY SAMPLING IS EMANABLE Non-probability sampling techniques are most appropriate for qualitative research. Qualitative research It involves recording, analyzing and attempting to uncover the deeper meaning and significance of human behaviour and experience, including contradictory beliefs, behaviours and emotions. Maximum variation purposive sampling technique can be used to develop a wider picture of any phenomenon. Data collection here may be carried out in several stages rather than once and for all. Critical case sampling may be used to investigate whether a phenomenon is worth investigating further, before adopting an expert sampling approach to examine specific issues further. Qualitative researchers do not base their research on pre-determined hypotheses. They clearly identify a problem or topic that they want to explore. Snowball sampling may be helpful in exploring potentially unknown characteristics that are of interest before settling on their sampling criteria. 10

12 RESEARCH SITUATIONS WHERE NON-PROBABILTY SAMPLING METHOD IS USED It can be used where a sample frame is not readily available and the research has to be done. It is a popular sampling technique in many areas of science that require voluntary human subjects, as well as human trials within the pharmaceutical industry. It is an effective sampling strategy in experimental research settings. It can be used when demonstrating that a particular trait exists in the population. It can be used when randomization is impossible like when the population is almost limitless. It can be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. It is also useful when the researcher has limited budget, time and workforce. This technique can also be used in an initial study which will be carried out again using a randomized, probability sampling. References 1. Creswell, J. W. (2009). Research design: Qualitative,quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage. 2. Mertens, D. M. & McLaughlin, J. A. (2004). Research and evaluation methods in special education. Thousand Oaks, CA: Corwin Press. 3. Trochim, W. M. K. (2006). Research methods knowledge base 11

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