What makes research trustworthy

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Hum Resour Health. Download references. You can also search for this author in PubMed Google Scholar. MT conceptualized the idea for the paper and discussed with LN, who wrote first draft. MT and ST made extensive comments on subsequent drafts. All authors have read and approved the final manuscript.

Correspondence to Lot Nyirenda. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Nyirenda, L. Using research networks to generate trustworthy qualitative public health research findings from multiple contexts. Download citation. Received : 24 January Accepted : 30 December Published : 21 January Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. The bigger the sample size the higher the likelihood that the results are precise.

After a sample size of around , gains in precision become less pronounced. Often, however, due to limited time and money approaching such a large sample might not be feasible. The homogeneity of the population further affects the desired sample size; a more heterogeneous population requires a larger sample to include the different sub-groups of the population to a satisfactory degree.

The response rate is a complementary measure to the sample size, showing how many of the suitable individuals in the sample have provided a usable response. In web surveys, response rates tend to be lower than in other types of surveys. Data can be collected either through primary or secondary sources, ie it can be collected for the purposes of the study or existing data can be utilised.

If existing data sets collected by another organisation or researcher is used, reflecting on how credible the data source is, and how usable it is for the study in question, is important.

Here, using common sense and Google if necessary takes you a long way. Validity refers to the extent to which a notion, conclusion or measurement is well founded and corresponds to reality. In other words, does it measure what it intends to measure?

As an example, a study intends to investigate gender discrimination of faculty and in so doing, looks at the number of cases of discrimination brought forward by female faculty. Yet, as the study does not look at the reason for these discrimination complaints — whether it was indeed gender or ethnicity, religion, age or sexual orientation — the conclusion cannot be drawn that gender discrimination has increased.

When conducting research there is often a tendency to seek to generalise the findings. Two key criteria have to be met for this to be possible. First, results are applicable only to the population of the study. In other words, if a study analyses student satisfaction among students in the UK, the findings cannot be generalised to campuses in, for example, France. Second, data must be collected via a probability sample, ie every unit of analysis, here every student in the UK, has the same chance of being included in the sample.

A study is internally valid if it is able to determine whether a causal relationship exists between one or more independent variables and one or more dependent variables Heffner, , i. A study is internally valid if there are as little confounding variables as possible. Confounding variables are variables that the researcher fails to control or eliminate, allowing the results to show false correlation Shuttleworth, Therefore, on the one hand, internal validity refers to how well the study is run, e.

On the other hand, internal validity determines how confidently it can be concluded that the change in the dependent variable was produced solely by the independent variable as opposed to extraneous ones ibid. External validity describes the ability to generalize a study, which is particularly threatened if people, places, or times are poorly chosen Trochim, As measuring an entire population is impossible, a sample, or a subset of the population is studied. The sample chosen needs to represent the whole population in order to allow inferences to be drawn Landreneau, A good sampling model firstly identifies the population that it should generalize, subsequently drawing a sample from that population, conducting research and finally generalizing the results back to the original population Trochim, External validity will improve the more a study is replicated ibid.

As quantitative methods are often used in natural science, the very fine tools and criteria developed for that realm may be adapted and used for business research. As an example, a set of eight criteria was developed to identify high quality evidence in the public health sector Effective Public Health Practice Project, Each of the criteria selection bias, study design, confounders, blinding, data collection methods, withdrawals and dropouts, intervention integrity, analysis is rated as strong, moderate, or weak, thus achieving an overall methodological rating ibid.