Orientation Observation In-depth interviews Document analysis and semiology Conversation and discourse analysis Secondary Data Surveys Experiments Ethics Research outcomes



Social Research Glossary

About Researching the Real World



© Lee Harvey 2012–2019

Page updated 25 January, 2019

Citation reference: Harvey, L., 2012–2019, Researching the Real World, available at
All rights belong to author.


A Guide to Methodology

8. Surveys

8.1 Introduction to surveys
8.2 Methodological approaches
8.3 Doing survey research

8.3.1 Aims and purpose
8.3.2 Background to the research
8.3.3 Feasibility
8.3.4 Hypotheses
8.3.5 Operationalisation
8.3.6 How will data be collected and what are the key relationships?
8.3.7 Designing the research instrument
8.3.8 Pilot survey
8.3.9 Sampling
8.3.10 Questionnaire distribution and interviewing
8.3.11 Coding data
8.3.12 Analysis Response rate Frequency tables Graphical representation Measures of central tendency (averages) Levels of measurement Crosstabulation Measures of dispersion Generalising from samples Dealing with sampling error Confidence limits Statistical significance Association Summary of significance testing and association: an example

8.3.13 Hypothesis testing
8.3.14 Significance tests
8.3.15 Report writing

8.4 Summary and conclusion Generalising from samples
The analysis of survey data undertaken so far has not attempted to make generalisations to wider populations from which the samples were taken. It is important to be aware that the data is for a sample, not a population, and analysis so far is about the sample.

One cannot simply transfer results for a sample onto the population from which it is drawn. Even representative samples are not identical replicas of the population but are liable to variation due to sampling error. The bigger the sampling error the less accurate your sample will be in estimating values for the population.

If the sample is not a representative sample (see Section 8.3.9 then it will be biased as well.

So making statements about wider populations must be done with care. It is important to be aware of both bias and sampling error when generalising about populations from samples.

There are a set of statistical procedures that enable accurate estimates of the impact of sampling error on random samples. These are called confidence limits and significance tests and are discussed in Section and Section

The other thing that to be aware of at this stage of the analysis is that although a relationship between two variables might have been identified, it is not possible to say how strong that relationship is on the basis of the analysis so far. Measuring the degree or extent of a relationship or an association can be done statistically (see Section


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