CREATING A PERFECT RESEARCH HYPOTHESIS FOR YOUR ACADEMIC RESEARCH
A research hypothesis is meant to be a simple and clear statement of prediction on the predicted outcome of an academic research effort. It describes the predicted outcomes in tangible (rather than theoretical) terms. However, not all studies are given hypotheses; some studies are designed to be exploratory, eliminating the need for formal hypotheses; and some studies are carried out to develop hypotheses for the purpose of future research; such studies do not have a predicted hypothesis, but the final result of such studies aids in the formulation of hypotheses to be tested in subsequent research. However, it should be underlined that every true experimental design must have this statement at the heart of its structure, as the ultimate goal of any investigation.
It becomes crucial to assess the overall research issue presented in the academic research article before drafting research hypotheses. This can be quite hard to define at times because many students have vague ideas about what they hope to get out of a study, which makes it hard to formulate a research topic that is both clear and concise. Glenn Firebaugh (2008) distinguished two essential standards for research inquiries:
- Research questions must be researchable: researchable means that a question can be answered through empirical research (that is, something that science can address) and that the topic is narrow enough that a study can hope to answer it in a reasonable amount of time.
- A research question should be fascinating. The condition that the research question be intriguing implies, first and foremost, that the issue be relevant to the ongoing scientific discussion of the topic.
Hypotheses in Quantitative Studies
For research hypotheses using categorical variables, it is possible to declare which category of the independent variable is connected with which category of the dependent variable. Continuous variables can also be described categorically (those with higher education are more likely to have high earnings).
Most researchers prefer to provide study hypotheses in a directed fashion, which means they make a statement about the expected link based on existing theory, previous research, general observation, or even an informed guess. It is also appropriate to employ the null hypothesis, which essentially indicates that there is no link between the variables; keep in mind that the null hypothesis serves as the foundation for all statistical tests of significance. A compromise position is to give a research hypothesis that outlines a potential direction for the relationship but softening the causal argument with words like “tends to” or “in general.”
Hypotheses in Qualitative Studies
Hypotheses in qualitative investigations differ significantly from those in quantitative studies. Hypotheses are not generated at the start of a qualitative study because of its inductive character. Instead, hypotheses are only provisionally presented during the iterative process of data collection and interpretation, and they assist the researcher in asking additional questions and looking for disconfirming evidence.
Qualitative research is guided by central questions and sub questions posed by the researcher at the outset of the study. These questions usually employ the language of how and what in an effort to allow understanding to emerge from the research, rather than why, which tends to imply that the researcher has already developed a belief about the causal mechanism. In general a qualitative study will have one or two central questions and a series of five to ten subquestions that further develop the central questions. These questions are often asked directly of the study participants (through in-depth interviews, focus groups, etc.) in recognition of the fact that developing an understanding of a particular phenomenon is a collaborative experience between researchers and participants.
Types of Hypothesis
We have two fundamental sorts of study hypotheses: null and alternative. The null hypothesis is presented in the negative form, for example, there is no significant association between employee motivation and job satisfaction among librarians in a university library. It is denoted with the Ho symbol. The alternative hypothesis is stated in a positive or affirmative manner, for example, there is a considerable association between employee motivation and job satisfaction among instructors in the university library. It’s indicated with the “Hi” sign.
Sources of Hypotheses
Hypotheses could be derived from the following sources (Akinade&Owolabi, 2009):
- Directly from research questions
- Directly from research designs
- Directly from research variables
- Review of literatures
- Directly from research objectives
- Directly from research designs
- Directly from findings of other studies
- From personal or practical experience or observation and analogy
- Personal Judgement influenced by your careful evaluation of the research question, research objective, research design and a review of past studies
- Logical reasoning
- Suggestions from students or colleagues or subject experts
A testable hypothesis makes a statement about a presumed or theoretical relation between two or more variables (Kerlinger, 1986) – that can be observed or measured. Thus, a testable hypothesis should state or imply that the variables are observable and measurable. It should also specify the relation among the variables, e.g. higher grades are obtained after studying in the library than studying in the hall of residence.
Non-testable hypotheses may include, “abortion is wrong“, “stigmatizing ebola patient is bad”, “I seem to study better in the library than in your room” (Akinade&Owolabi, 2009, p. 19).
Testing of Hypotheses
The following steps should be observed when testing any research hypothesis (Awoniyi et al, 2011):
- Formulate a null hypothesis (Ho). Hypothesis can either be accepted or rejected.
- Set up a suitable level of significance. It means the level at which to accept or reject null hypothesis. It is conventional among researchers to accept or reject hypothesis at 0.05 or 0.01 level of significance. This implies that the researcher is allowed some margin of error in his research result. For instance 0.05 level of significance implies that the researcher allows 5% error margin and he is 95% confident of whatever result or conclusion is drawn from his study while for 0.01 level of significance the researcher allows only 1% error margin and he is 99% confident of the result or conclusion drawn from his study.
- Select appropriate statistical techniques. There are many techniques from which a researcher can choose the most appropriate to test his hypothesis such as in the following cases:
- When hypothesis has to do with a large sample (i.e. more than 30) and it is testing for a significant difference between two variables, the z – test implying normal distribution is used and when a sample is small (less than 30), t – test is employed.
- When the hypothesis is started to test for relationship between two variables, the Pearson product moment correlation co-efficient is used, when there is equal distribution of subjects for non-parametric variables, the Spearman rank order correlation coefficient is used.
- When the hypothesis is stated to test differences or relationship between two or more independent variables and one dependents variable, the analysis of variance (ANOVA) or analysis of covariance (ANCOVA) could be employed.
- Multivariate Analysis of Covariance (MANCOVA) is used when there are two or more independent variables against two or more dependent variables.
- Chi-square test is applied to data derived from normal scale of measurement, that is, data obtained in form of frequency counts. It is used for testing hypothesis concerning the difference between sample frequency observed within certain categories and those expected with the categories and those expected with the categories. Chi-square can only indicate whether or not a set of observed frequencies differ significantly from the corresponding set of expected frequencies.
Characteristics of Good Hypotheses
According to Awoniyi et al (2011); Akinade and Owolabi (2009); Cherry (2014), each acceptable hypothesis should have the following features:
- It must provide a specific and adequate answer to a problem that is limited in scope (i.e. give only one answer to a problem at a time).
- It must be based on the research topic.
- It must include independent and dependent variables. It should be framed in a way that the expected relationship between independent and dependent variables are clearly stated.
- Hypotheses should be testable and measurable. One of the most common sources of difficulty for the student who embarks on research project is the selection of hypothesis that is not really testable.
- It should be correctly, clearly and precisely stated.
- It should state expected relationships between research variables. a research hypotheses should be appropriate as a basis for research.
- It should be limited in scope. A common error of the research student in planning research is to develop hypotheses of global significance. Students should seek hypotheses that are relatively simple to test, and yet are highly significant.
- It should be consistent with most facts. Any hypotheses formulated as a basis of research must be consistent with a substantial body of established facts.
- The hypotheses selected should be amendable to testing within a reasonable time. Before any research is undertaken, the student should ask himself practical questions about whether he has the resources and time to undertake the investigation.
- Hypotheses must be testable or verifiable by independent researchers using identical conditions, so as to ascertain its validity.
- Hypotheses should be tested without violating ethical standards.
- Hypotheses should be operationally defined.
Understanding some myths surrounding research hypotheses
Myth: A hypotheses is an educated guess
In everyday language, the word ‘hypotheses’ refers to an intelligent guess. In science, it might be misunderstood to indicate an assumption made prior to doing an experiment or a notion that has not yet been tested. In science, a hypothesis is defined as ‘a speculative explanation for a scientific problem, based on currently accepted scientific understanding and creative thinking’. Lines of evidence support research hypotheses, which are founded on scientists’ prior experience, background information, and observations.
Myth: Hypotheses become theories that, in turn, become laws
Hypothesis, theory, and law are three concepts that are frequently confounded. According to this myth, facts and observations generate hypotheses, which lead to theories, which, if enough evidence is gathered, establish laws – hence laws are theories that have been proven true.
Actually, hypotheses, theories, and laws are as different as apples, oranges, and bananas. They cannot develop into each other. Theories and laws are fundamentally different sorts of knowledge. Empirical evidence establishes natural laws, which are generalizations, principles, correlations, or patterns. Theories are explanations for those generalisations (which are supported by actual facts).
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Awoniyi, S.A., Aderanti, R.A. &Tayo, A.S. (2011).Introduction to Research Methods.
Ibadan: Ababa Press.
Cherry, K. (2014).What exactly is a Hypothesis? Retrieved from http://psychology.about.com/od/hindex/g/hypothesis.htm on 22/12/2014.
Glenn, F. (2008). Forming a Good Hypothesis for Scientific Research Retrieved from https://www.verywellmind.com/what-is-a-hypothesis-2795039 on 2019-02-14
Guevera, L. (2010) Myths of the nature of science Retrieved from https://www.sciencelearn.org.nz/resources/415-myths-of-the-nature-of-science on 2019-02-14
Kerlinger, F.N. (1986). Behavioural Research: A Conceptual Approach. New Delhi: Sterling Publisher.