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## Sampling Techniques

### Sample Types and Sample Size

This chapter assesses sampling techniques. Researchers may restrict their data collection to a sample of a population for convenience or necessity if they lack the time and resources to collect data for the entire population. Therefore, a sample is any subset of units collected from a population. Research sampling techniques refer to case selection strategy — the process and methods used to select a subset of units from a population. While sampling techniques reduce the costs of data collection, they induce a loss in terms of comprehensiveness and accuracy, compared to working on the entire population. The data collected are subject to errors or bias. Two main decisions determine the size or margin of error and whether the results of a sample study can be generalized and applied to the entire population with accuracy: the choice of sample type and the sample size.

## 10. Sampling

Whether the research project adopts a quantitative, qualitative, or mixed strategy, there is little point in asking a few non-random people a few non-random questions as the student has no idea what those answers might indicate, or whether they might apply in other situations. Therefore, the student needs to think carefully about his or her sampling strategy and justify this in the dissertation. This chapter explains the key principles of probability and non-probability sampling and explores why ‘who’ is asked is just as important as ‘what’ is asked. It discusses the two key stages of sampling: defining the appropriate population for study and developing strategies for recruiting the sample.

## Statistical Significance

This chapter highlights statistical significance. The key question in quantitative analysis is whether a pattern observed in a sample also holds for the population from which the sample was drawn. A positive answer to this question implies that the result is ‘statistically significant’ — i.e. it was not produced by a random variation from sample to sample, but, instead, reflects the pattern that exists in the population. The null hypothesis statistical test (NHST) has been a widely used approach for testing whether inference from a sample to the population is valid. Seeking to test whether valid inferences about the population could be made based on the results from a single sample, a researcher should consider a wide variety of approaches and take into the account not only p-values, but also sampling process, sample size, the quality of measurement, and other factors that may influence the reliability of estimates.

## 11. Surveys

This chapter discusses the principles of survey research as well as the issues and problems associated with different stages of the research design process. In particular, it examines questionnaire design, sample design, and interviewing techniques, along with the common sources of error that affect survey research and what can be done to try and avoid or minimize them. Although surveys have several weaknesses, they are widely used in political research to investigate a wide range of political phenomena. They combine two things: obtaining information from people by asking questions and random sampling. When done well, surveys provide an accurate and reliable insight into what ordinary people think about politics and how they participate in politics. The chapter considers the elements of a survey that need to be addressed, namely: questionnaire design, measurement error, sampling design, sampling error, and interview mode.

## Bayesian Inference

This chapter evaluates Bayesian inference, which refers to the Bayesian statistical method for estimating the parameters of a model and for testing a hypothesis. It relies on subjective statistics and extensively uses Bayes’s theorem. In the early 1990s, Bayesian statistics boomed with the emergence of sampling techniques. These new tools rely on the computational power to sample from (rather than evaluate) the posterior probability. However, the main drawback of the Bayesian approach lies in the computation of the posterior probability. The analytical computation of the posterior probability is a complex problem for any application, and this has limited Bayesian statistics for years.

## How to do your Social Research Project or Dissertation (1st edn)

How to do your Social Research Project or Dissertation looks to help readers to navigate research for a project or dissertation. It starts with an introduction to the research process and how to get started. It examines the process of developing an idea. It reviews the available literature. It then considers how to build upon the project idea, the ethical issues, and how to write a proposal. Next it considers sampling, and collecting and analyzing quantitative and qualitative data. Finally, it describes how to evaluate the project and the process of writing up.

## 13. Ethnography and Participant Observation

This chapter discusses the principles of ethnography and participant observation: what they are, how (if) they became standardized as a research method, what form of evidence they constitute, and what place they occupy in the study of Politics. Participant observation has emerged as a popular research tool across the social sciences. In particular, political ethnographies are now widely carried out in a broad variety of contexts, from the study of political institutions and organizations to the investigation of social movements and informal networks, such as terrorist groups and drugs cartels. Political ethnography is also becoming a research method of choice in the field of International Relations. The chapter examines the strengths of ethnographic fieldwork, focusing on issues relating to sampling, access, key informants, and collecting observational data. It also addresses the weaknesses of ethnography, especially issues of subjectivity, reliability, and generalizability.

## Unit of Analysis and Observation

This chapter addresses the unit of analysis and observation. Each empirical social or behavioural science study typically includes the identification of one or more units of analysis. The unit is the entity, element, or grouping that constitutes the focus of the study’s analyses, and multiple cases of this unit are analysed. The unit of analysis is of primary importance, as this is the unit that is referred to in hypotheses or research questions and therefore the unit that is the focus of data analyses that address these hypotheses or research questions. However, there are two other types of units that need to be considered. In sum, the three types of units in any empirical study are the unit of sampling, the unit of observation or measurement (sometimes called the unit of inquiry or unit of data collection), and the unit of analysis.