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Answer
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Hint
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Operationalising Concepts
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This is the second step for researching. Suppose our hypothesis is that working-class pupils achieve lower qualifications because of lower parental income. Before we can test it, we need a working or 'operational' definition of our key ideas - in this case, social class. The reason is simple: without a working definition, we won't be able to count the numbers of working-class pupils who have or don't have qualifications. Now, 'social class', is a fairly abstract concept, so we need a way of measuring what class each pupil belongs to. Most sociologists would probably use parental occupation as an indicator of a pupil's social class. This process of converting a sociological concept (such as class) into something we can measure is called 'operationalisation'. Once we have operationalised our concept, we can start devising questions that measure it. For example, we might ask parents, 'what is your job?'. This will allow us to see what social class each pupil belongs to. We can then correlate this with information we collect about their qualifications to find out whether our hypothesis is true or false. This may seem straightforward. But a problem can arise when different sociologists do this for the same concept differently. For example, we may disagree about whether a routine office worker is working-class or middle-class. This can make it hard to compare the findings of different pieces of research. Positivists are concerned to this because of the importance they place on creating and testing hypotheses. By contrast, interpretivists put less emphasis on this, because they are more interested in actors' own definitions and understandings of ideas such as 'class', 'achievement' etc, than in imposing their own definitions of these concepts. |
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The Pilot Study
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This might be the third step for researching. Sociologists who use social surveys (questionnaires and unstructured interviews) often carry out this before conducting their main survey. This involves trying out a draft version of the questionnaire or interview schedule (the list of interview questions) on a small sample. The basic aim of this is to iron out any problems, refine or clarify questions and their wording and give interviewers practice, so that the actual survey goes as smoothly as possible. For example, Young and Willmott (1962) carried out just over 100 pilot interviews to help them decide on how the design of their study, the questions to ask and how to word them. This may reveal that some questions are badly worded and hard to understand, or that the answers are difficult to analyse. After carrying out this, it should be possible to finalise the questionnaire or interview schedule. |
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Sampling Frame
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To choose a sample, we first need this. This is a list of all the members of the population we are interested in studying. For example, Young and Willmott used the electoral register (the list of people entitled to vote) as theirs. It is important that the list we use as a this is as complete and accurate as possible. It should also be up to date and without duplications - otherwise the sample chosen from it may not be truly representative of the population. Once we have obtained this, we can choose our sample from it. In selecting the sample, we need to ensure it is representative of the wider population we are interested in. |
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Quota Sampling
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This is a sampling technique. The population in the sampling frame is stratified by class, gender, age etc. Each interviewer is given a quota (for example, 20 males & 20 females) which they must fill with respondents who fit these characteristics. The interviewer continues this task until their quota is filled. This type of sampling is simple and easy to execute, and is also generally representative of the research population (unlike snowball, opportunity and volunteer sampling). However, this sampling type is more likely to produce samples which are biased. |
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Systematic Sampling
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This is a sampling technique, which is also known as quasi-random sampling. Every 'nth' person in the sample frame is selected for the sample. For example, every 5th person in the sampling frame. Young and Willmott used every 36th name on the electoral register for their sample. This type of sampling is easy to execute, and the risk of clustered selection is eliminated. However, there is a higher risk of data manipulation, as some patterns identified from this may not be representative of the whole research population. |
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Aim
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The first step is to formulate this for the research. This is more general than a hypothesis, as it identifies what we intend to study and hope to achieve through the research. Often it will simply be to collect data on a particular topic, such as the way of life of a subculture. The advantage of formulating with this is that it is more open-ended. We are not tied to trying to prove a particular hypothesis; instead we can gather data on anything that appears interesting about a situation. This can be very useful at the start of our research, when we know very little about the topic - since by definition, in this situation we would have no real idea about what hypothesis we wanted to test. Interpretivists often favour a broad type of this rather than a hypothesis, since they are interested in understanding actors' meanings, so the task is to find out what the actors themselves think is important, rather than to impose the researcher's own possible explanations in the form of a hypothesis. |
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Sampling
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This might be the third or fourth step for researching. Sociologists often aim to produce generalisations that apply to all cases of the topic they are interested in. For example, if we were interested in educational achievement, we would ideally want our theory to explain the achievement levels of all pupils, not just the ones who were in our study. Obviously, however, we do not have the time or money to include every pupil in the UK in our study, so we have to choose a sample of pupils to include. The basics of this process is usually to ensure that those people we have chosen to include in the study (such as pupils) are representative or typical of the research population, including all the people we have not been able to include in the study (the research population refers to the whole group that we are interested in - all pupils, in this case). So long as our sample is representative, we should be able to generalise our findings to the whole research population. This is particularly attractive to positivist sociologists, who wish to make general, law-like statements about the wider social structure. |
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Hypothesis
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The first step is to formulate this for the research. Most studies have this in general. This term is a possible explanation that can be tested by collecting evidence to prove it true or false. For example, we may suspect that family size affects educational achievement. If so, we can formulate this specifically as a cause-and-effect statement, such as: 'differences in family size cause differences in achievement'. We can then collect evidence to test whether or not this is true. If it turns out to be false, we must discard it. Discarding it might seem like a bad thing, but in fact it means we have made some progress. For example, if our research reveals no link with family size, we have learned something new and so we can now turn our attention to another possible cause instead - perhaps parental attitudes, or income? We simply formulate a new one of this and set out to test it. The advantage of this is that it gives direction to our research. It will give a focus to our questions, since their purpose is to gather information that will either confirm or refute (disprove) it.
Positivists favour using this as the starting point for research. This is because they seek to discover cause-and-effect relationships - e.g. that large family size causes underachievement. Using quantitative methods such as questionnaires, they formulate questions designed to discover whether and why these factors are linked. |
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Opportunity Sampling
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This is a sampling technique, which is also known as convenience sampling. This is where the sample is collected from individuals who are easiest to access. For example, by selecting passers-by in the street, or a captive audience such as a class of pupils. This type of sampling is quick and easy to set up as it may just require talking to random people (which become part of the sample if they take part). However, it is quite unlikely to be representative of the target research population. |
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Sample
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This term is a small sub-group drawn from the wider group that we are interested in.
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Reasons for not creating a Representative Sample
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Practical Reasons: -The social characteristics of the research population, such as age, gender and class, may not be known. It would thus be impossible to create a sample that was an exact cross-section of the research population. -It may be impossible to find or create a sampling frame for that particular research population. For example, not all criminals are convicted, so there is no complete list available from which to select a sample. -Potential respondents may refuse to participate. For example, some criminals may refuse for fear that their responses may be passed to the police. Theoretical Reasons: -Even where it is possible to create a representative sample, some researchers may not choose to do so, because of their methodological perspective. Interpretivists believe that it is more important to obtain valid data and an authentic understanding of social actors' meanings than to discover general laws of behaviour. Because they are less concerned to make generalisations, they have less need for representative samples. |
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Volunteer Sampling
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This is a sampling technique. The researcher will advertise for volunteers to take part in the sample. This type of sampling makes it easier to use hard-to-reach populations in the sample. However, it may not be representative and more prune to biased results. A question which the researcher could ask themselves is 'Why might each individual take part?'. |
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Random Sampling
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This is a sampling technique. A sample is selected purely by chance - for example, names could be drawn out of a hat. Everyone has an equal chance of being selected for the sample. A large enough of this type of sample should reflect the characteristics (e.g. gender, class, etc.) of the whole research population. However, not all of these type of samples are large enough to ensure this happens. |
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Snowball Sampling
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This is a sampling technique. This is where the sample is collected by contacting a number of key individuals, who are asked to suggest others who might be interviewed, and so on, adding to the sample 'snowball' fashion, until enough data has been collected. This type of sampling is a useful way to contact a sample of people who might otherwise be difficult to find/persuade to take part (for example, criminals and prisoners). However, it is extremely unlikely to be representative of the target research population. |
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Stratified Random Sampling
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This is a sampling technique. A researcher stratifies (breaks down) population in the sampling frame by age, gender, class etc. The sample is then created in the same proportions. For example, if 20% of the population are under 18, then 20% of the sample also have to be under 18. This type of sampling produces characteristics in the sample which are proportional to the research population. However, it is difficult to use when researchers can't classify every member of the sampling frame into a certain subgroup. |
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