In qualitative research, reliability can be evaluated through: Finally at the data analysis stage it is important to avoid researcher bias and to be rigorous in the analysis of the data (either through application of appropriate statistical approaches for quantitative data or careful coding of qualitative data). At the implementation stage, when you begin to carry out the research in practice, it is necessary to consider ways to reduce the impact of the Hawthorne effect. It is also necessary to consider validity at stages in the research after the research design stage. It is necessary to consider how effective the instruments will be in collecting data which answers the research questions and is representative of the sample. For example it is important to be aware of the potential for researcher bias to impact on the design of the instruments. The design of the instruments used for data collection is critical in ensuring a high level of validity.
The chosen methodology needs to be appropriate for the research questions being investigated and this will then impact on your choice of research methods. A wide range of different forms of validity have been identified, which is beyond the scope of this Guide to explore in depth (see Cohen, et. Validity should be viewed as a continuum, at is possible to improve the validity of the findings within a study, however 100% validity can never be achieved. The table below compares the factors influencing validity within qualitative and quantitative research contexts (Cohen, et al., 2011 and Winter, 2000):Īppropriate statistical analysis of the data The validity of the research findings are influenced by a range of different factors including choice of sample, researcher bias and design of the research tools.
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