The Implications of Formulating a Hypothesis

In this step, we will look at the implications of formulating a hypothesis in your research.

This is important because, as with all research, this choice represents certain assumptions and it presupposes that your research will take a certain form.

What Are the Assumptions Behind Using Hypotheses?

We introduced the term ‘positivism’ in the last step to refer to a philosophical approach which is often associated with research that uses hypotheses. Positivism is a complex concept, but for our purposes, the key thing to know is that it believes there are objective truths that exist whether we know about them or not. The job of the researcher is to uncover these truths so we can advance human understanding.

To do this, the researcher should test what we consider to be true and if they can’t show it to be false then by definition we can consider it to be true. In this way, the hypothesis becomes the objective truth that is to be tested and verified. This is important because if you use hypotheses in your research, you have to believe that however limited your research, you are seeking to establish a truth that can’t be disputed because your evidence is objective.

This leads to a second implication of using hypotheses – namely that objective evidence is often considered to be numerical or quantitative.

Numbers have universal application, we can all objectively agree that 1+1=2 and it would be very hard to show that to be false and so it is often the case that the data collected for research projects that involve hypotheses can be easily translated into numerical quantities.

Doing so opens up a range of mathematical forms of analysis such as statistical analysis or probability with the aim of testing the hypothesis in many different ways. Importantly, not all research that uses hypotheses is quantitative, but it is more likely to be. Moreover, just because you don’t use hypotheses does not mean you can’t do a quantitative study, it is simply that if you want to measure the relationship between variables it is often easier to do so if your data can be converted into numbers (ie, through a Likert scale in a survey).

If we develop this point we see another implication of the use of hypotheses. If they propose a relationship between variables, you may have to explore different numerical tests to establish the strength or otherwise of that relationship. This typically means using specific statistical tests that are accepted techniques for measuring these relationships and many of these require a certain volume of data to be meaningful.

So, the final implication of using hypotheses in your research is that you need to be confident that you can generate sufficient data to be confident that you can say your hypothesis is true in all circumstances – or in the circumstances that you have defined.

The central strength of quantitative research that uses hypotheses is that it can be analysed in a way that proves it is true (or probably true) for a whole population. But testing the probability of a data set requires a certain threshold to be passed in terms of a quantity of data.

We can illustrate these points by returning to the example of Huselid (1995), introduced in the previous research. As we saw, he wanted to establish an objective truth that HRM practices were linked to financial performance (amongst other things). To test his hypothesis he sent a survey to HR managers in over 3000 firms, asking about which HRM practices they used. He received 968 responses. He also gathered data on the financial performance of these firms so he could compare the responses with this information.

His results were promising and are one of the reasons this is one of the most cited academic papers in the field. Huselid conducted a series of statistical tests on his data and showed that firms that introduced more HRM practices could see sales go up by \$27,044. Such a result was possible because Huselid formulated his research using testable propositions.

A final word of caution here, because when using hypotheses there are sometimes things you can’t do. Despite Huselid’s apparent success, many question his research and methods, often pointing out that he was not able to say why there was this link. As a result, his research was criticised for not having any practical application because it did not really help managers know what they should do with it. So, using hypotheses is a powerful way of meeting your research objectives, but you need to be clear about their limitations as well.

Can you think of any other concerns with research like that of Huselid?
How could he have developed his research to address some of these concerns?

References

Husselid, M. A. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38 (3), 635-672. DOI: 10.2307/256741 Web link

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