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Research Review Inventory:
Research design This term is employed in this book to refer to a framework for the collection and analysis of data. A choice of research design reflects decisions about the priority being given to a range of dimensions of the research process (such as causality and generalization).
Research question A research question is a question that provides an explicit statement of what it is the researcher wants to know about.
Research strategy A term used in this book to refer to a general orientation to the conduct of social research (see quantitative research and qualitative research).
Paradigm is an approach or a research model to conducting a research that’s been verified by the research community. Paradigm as a model or pattern containing a set of legitimate assumptions and a design for collecting and interpreting data. A model or framework for observation and understanding, which shapes both what we see and how we understand it. It is the style of asking questions and giving answers which, we adopt. There are many different paradigms, but many identify three broad classes: positivism , interpretivist , constructionist and objectivism. Components to research paradigm: Ontology , epistemology and methodology
Epistemological concerns the question of what is (or should be) regarded as acceptable knowledge in a discipline. In social science research methods, our main epistemological issues are: can, and should, the social world be studied in the same way as the natural sciences? If yes: Positivism. If no: Interpretivism. In other words, epistemology refers to researchers’ assumptions about how to gain knowledge about the social world, for example positivist researchers assume that one should measure social phenomena objectively.
Positivism is an epistemological position that advocates the application of the methods of the natural sciences to the study of social reality and beyond. It entails the following principles:
- Only phenomena, hence, knowledge confirmed by the senses can be genuinely be warranted as knowledge (principle of phenomenalism)
- Associated with deductive approach (generate hypotheses that can be tests & allow explanations of laws to be assessed)
- Knowledge arrived through generating facts that provide the basis for laws (Inductivism)
- Science conducted that’s objective/ value-free
- Carries influences to varying degrees in the social sciences, notably in economics
- (^) There is a clear distinction between scientific statements and normative statements and a belief that the former is the true domain of the scientists.
Interpretivism a contrasting epistemology to positivism. The term subsumes the views of writers who have been critical of the application of the scientific model to the study of the social world. They share a view that the subject matter of the social sciences – people and their institutions – is fundamentally different from that of natural sciences. The study of the social world therefore requires a different logic of research procedure, one that reflects the distinctiveness of humans as against the natural order. Von Wright depicted the epistemological clash between positivism and hermeneutics (concerned with the theory and method of the interpretation of human action). This clash reflects: explanation of human behaviour (P) vs understanding of human behaviour (I). The latter is concerned with the empathic understanding of human action rather than with the forces that are deemed to act on it.
Ontology concerned with the nature of social entities. The central point or orientation is the question of whether social entities can and should be considered objective entities that have a reality external to social actors (the research takes the position of objectivism : social phenomena confronts us as external – independent and objective – facts), or whether they can and should be considered social constructions build up from the perceptions and actions of social actors (the research takes a position of constructivism : social phenomena and their meanings are continually being accomplished by social actors; the phenomena are not only produced through social interactions, but they are in a constant state of revision). Ontology refers to researchers’ assumptions about the nature of the social world, for example, positivist researchers tend to assume that the social world operates in a predictable, law-like manner analogous to the physical world.
Objectivism is an ontological position that implies that social phenomena confronts us as external facts that are beyond our reach or influence. For example: an organization has rules and regulations; standardized procedures for getting things done; there is a hierarchy; mission statement; individuals have to conform to these rules; they do jobs to which they are appointed; they are told what to do and they tell others what to do; otherwise, they may be reprimanded or even fired. It asserts that social phenomena and their meanings have an existence that is independent of social actors. Social phenomena confront us as external facts. Individuals are born into a pre-existing social world. Social forces and rules exert pressure on actors to conform. Cultures and subcultures can be viewed as repositories (where things are/may be stored) of widely shared values and customs into which people are socialized so that they can function as good citizens or as full participants. Cultures/subcultures constrain use because we internalize their beliefs and values. Objectivism refers to the belief that virtually all humans understand reality in the same manner.
Constructionism an ontological position that asserts that social phenomena and their meanings are continually being accomplished by social actors. It implies that social phenomena and categories are not only produced through social interactions, but they are in a constant state of revision. For example: in some organization’s rules are less extensive and less rigorously imposed than in other classic organizations – they are not commands but rather general understandings; the outcome are agreed-upon patterns of action in different situations that are product of negotiations among the different parties involved. The social order is in a constant state of change for example: in a hospital, everyday interactions may be more important than the official rules. Social phenomena and their meanings are constructed by social actors. Continually accomplished and revised. Researcher’s accounts of events are also constructions – many alternative interpretations
Methodology refers to the actual procedures used by researchers, for example positivist researchers frequently collect data by means of objective tests and questionnaires and analyse these using statistical techniques.
Probability Sampling Methods
- Simple random sampling
In this case each individual is chosen entirely by chance and each member of the population has an equal chance, or probability, of being selected. One way of obtaining a random sample is to give each individual in a population a number, and then use a table of random numbers to decide which individuals to include.1 For example, if you have a sampling frame of 1000 individuals, labelled 0 to 999, use groups of three digits from the random number table to pick your sample. So, if the first three numbers from the random number table were 094, select the individual labelled “94”, and so on.
As with all probability sampling methods, simple random sampling allows the sampling error to be calculated and reduces selection bias. A specific advantage is that it is the most straightforward method of probability sampling. A disadvantage of simple random sampling is that you may not select enough individuals with your characteristic of interest, especially if that characteristic is uncommon. It may also be difficult to define a complete sampling frame and inconvenient to contact them, especially if different forms of contact are required (email, phone, post) and your sample units are scattered over a wide geographical area.
- Systematic sampling
Individuals are selected at regular intervals from the sampling frame. The intervals are chosen to ensure an adequate sample size. If you need a sample size n from a population of size x, you should select every x/nth individual for the sample. For example, if you wanted a sample size of 100 from a population of 1000, select every 1000/100 = 10th member of the sampling frame. Systematic sampling is often more convenient than simple random sampling, and it is easy to administer. However, it may also lead to bias, for example if there are underlying patterns in the order of the individuals in the sampling frame, such that the sampling technique coincides with the periodicity of the underlying pattern. As a hypothetical example, if a group of students were being sampled to gain their opinions on college facilities, but the Student Record Department’s central list of all students was arranged such that the sex of students alternated between male and female, choosing an even interval (e.g. every 20thstudent) would result in a sample of all males or all females. Whilst in this example the bias is obvious and should be easily corrected, this may not always be the case.
- Stratified sampling
In this method, the population is first divided into subgroups (or strata) who all share a similar characteristic. It is used when we might reasonably expect the measurement of interest to vary between the different subgroups, and we want to ensure representation from all the subgroups. For example, in a study of stroke outcomes, we may stratify the population by sex, to ensure equal representation of men and women. The study sample is then obtained by taking equal sample sizes from each stratum. In stratified sampling, it may also be appropriate to choose non-equal sample sizes from each stratum. For example, in a study of the health outcomes of nursing staff in a county, if there are three hospitals each with different numbers of nursing staff (hospital A has 500 nurses, hospital B has 1000 and hospital C has 2000), then it would be appropriate to choose the sample numbers from each hospital proportionally (e.g. 10 from hospital A, 20 from hospital B and 40 from hospital C). This ensures a more realistic and accurate estimation of the health outcomes of nurses across the county, whereas simple random sampling would over-represent nurses from hospitals A
and B. The fact that the sample was stratified should be taken into account at the analysis stage. Stratified sampling improves the accuracy and representativeness of the results by reducing sampling bias. However, it requires knowledge of the appropriate characteristics of the sampling frame (the details of which are not always available), and it can be difficult to decide which characteristic(s) to stratify by.
- Clustered sampling
In a clustered sample, subgroups of the population are used as the sampling unit, rather than individuals. The population is divided into subgroups, known as clusters, which are randomly selected to be included in the study. Clusters are usually already defined, for example individual GP practices or towns could be identified as clusters. In single-stage cluster sampling, all members of the chosen clusters are then included in the study. In two-stage cluster sampling, a selection of individuals from each cluster is then randomly selected for inclusion. Clustering should be taken into account in the analysis. The General Household survey, which is undertaken annually in England, is a good example of a (one-stage) cluster sample. All members of the selected households (clusters) are included in the survey.
Cluster sampling can be more efficient that simple random sampling, especially where a study takes place over a wide geographical region. For instance, it is easier to contact lots of individuals in a few GP practices than a few individuals in many different GP practices. Disadvantages include an increased risk of bias, if the chosen clusters are not representative of the population, resulting in an increased sampling error.
Non-Probability Sampling Methods
- Convenience sampling
Convenience sampling is perhaps the easiest method of sampling, because participants are selected based on availability and willingness to take part. Useful results can be obtained, but the results are prone to significant bias, because those who volunteer to take part may be different from those who choose not to (volunteer bias), and the sample may not be representative of other characteristics, such as age or sex. Note: volunteer bias is a risk of all non-probability sampling methods.
- Quota sampling
This method of sampling is often used by market researchers. Interviewers are given a quota of subjects of a specified type to attempt to recruit. For example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing. Ideally the quotas chosen would proportionally represent the characteristics of the underlying population.
Whilst this has the advantage of being relatively straightforward and potentially representative, the chosen sample may not be representative of other characteristics that weren’t considered (a consequence of the non-random nature of sampling). 2
- Judgement (or Purposive) Sampling
Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate. Researchers may implicitly thus
