Research Hypothesis

Research Hypothesis

A hypothesis is a guess or an assumption. It is a tentative explanation for certain behaviour patterns, phenomena, or events that have occurred or will occur (Gay, 1996). For example, price increase influences commodity consumption. This is only a guess. It may or may not be true, or it may be applicable to some commodities and not others. In this guess, a relationship is perceived between price increase and commodity consumption. It therefore has to be verified. In research, a hypothesis is a statement that describes an unknown but tentatively reasonable outcome for the existing phenomenon. It is a tentative answer to what the researcher considers as ought to be the possible outcome of an existing problem or phenomenon. It is a likely solution to a problem being studied, which is advanced before the actual research is undertaken. Orodho and Kombo (2004) define hypothesis as educated guesses about possible differences, relationships or causes of research problems. They state what the researcher thinks the outcome of the study will be.

Types of Hypothesis

There are three types of hypothesis: the conceptual, research and statistical hypothesis.

CONCEPTUAL HYPOTHESIS

This is a statement about the relationship between theoretical concepts. These are mainly ideas that can never be directly tested because they cannot be measured. They must be operationalized or made measurable before they are tested. For example, discipline facilitates academic achievement or negative attitudes retard development.

RESEARCH HYPOTHESIS

This is a statement about the expected relationship between observable or measurable events. An experimental research hypothesis states expected relationships between independent and dependent variables. For example, rewards after an accomplishment of a task will increase the frequency of the performance of the task. This is an example of an experimental research hypothesis.

For example, a teacher notes that students who complete the mathematics examinations half an hour before the expected time usually perform poorly as compared to those who complete in the expected time. The teacher may decide to investigate the relationship between the number of minutes needed to complete an examination and the score on the examination. The teacher may use the data to determine whether there is a significant negative relationship between these two variables. The research hypothesis may be formulated as follows: The length of time needed to complete tile mathematics examination will be negatively correlated with the score on the examination for students.

STATISTICAL HYPOTHESIS

This hypothesis states an expected relationship between the numbers representing statistical properties of data such as the mean, variance and correlation. This hypothesis is a guess about the value of a population parameter or about the relationship between values of two or more parameters the hypothesis is testing. The statistical hypotheses consist of the null hypothesis (H0) and the alternative hypothesis (H1). An example of a statistical hypothesis can be stated as following:

The mean different scores in Sociology by students in the Institute of Open Learning and those in the Department of Sociology at Kenyatta University is zero.

Ways of Stating the Hypothesis

There are two forms of stating the hypothesis: the null and alternative forms.

THE NULL HYPOTHESIS

The null hypothesis states that there is no difference between the variables studied. The aim of testing is to show that the hypothesis is false and thereby accept the alternative hypothesis. The null hypothesis refers to the guess the researcher tests and hopes to prove wrong, reject or nullify. The null hypothesis states that no relationship exists between the variables studied. Confirmation of the research hypothesis is based on rejecting the null. For example, there is no significant difference in the academic performance of students who attend private schools and those who attend public schools in national examinations.

If the researcher wishes to show that a difference exists in national examination performance among students. in public and private schools, then the researcher must prove that there are no differences. The null hypothesis specifies the expected value of a single population parameter or the expected relationship between two or more parameters.

The first step in testing a hypothesis is to make the assumption that there is no significant difference between variables or conditions being studied. This assumption is called Null and it refers to nothing or no relationship. Null is symbolized by H0.

The aim of testing is to show that the hypothesis is false and thereby accept the alternative one. The null hypothesis states that no relationship exists between the variables being studied. Confirmation of the research hypothesis is based on rejecting the null.

Examples:

H01: There is no significant difference in the academic performance of students who attend private schools and those who attend public schools in national examinations.

If the researcher wishes to show that a difference in performance exists in national examinations among students in public and private schools, then he/she must prove that there are no differences.

Other examples are:

H02: There is no significant difference between an individual’s success in life and his/her academic certificates.

H03: There is no significant difference between business locale and profit margin.

H04: There is no significant difference in performance between female and male entrepreneurs.

H05: There is no significant difference between the behaviour of female and male pastors.

H06: There is no significant difference between managerial skills of male and female managers.

Null hypotheses specify the expected value of single population parameter or the expected relationship between two or more parameters. Therefore, it is important to note that all the hypotheses should be tested. There is no way a verdict can be passed without an investigation.

THE ALTERNATIVE HYPOTHESIS

This hypothesis states a value or relationship and it is different from the null. It asserts that the value of relationship in the null is not ‘cruel in research, the null hypothesis is tested, and if rejected, the alternative hypothesis is accepted.

Alternative hypothesis is the opposite of null and it is symbolized by H1.

Examples:

H1: There is a significant difference between the perception and attitude of entrepreneurs.

H2: There is a significant difference between success in business and determination.

H3 Teachers determine the success or failure of their students in life.

All stated hypotheses require testing. Therefore, it is imperative for a researcher to know that all the hypotheses should be backed up by evidence.

DIRECTIONAL HYPOTHESIS

If the researcher’s interest is in finding a difference only in a particular direction, then a directional hypothesis is used. A directional hypothesis states the relationship between the variables being studied or difference between experimental treatments that a researcher expects to emerge. For example if a researcher is interested in finding out how teacher qualifications influence students’ performance in mathematics in secondary schools, the directional hypothesis can be stated as following: There is a positive and significant relationship between the qualification of teachers and student performance in mathematics in secondary schools.

Importance of Hypotheses in Research

The hypothesis plays a vital role in research. This includes the following:

  1. It states the researcher’s expectations concerning the relationship between the variables in the research problem.
  2. The hypothesis refines the research problem.
  3. By defining the variables in the study, the hypothesis enables the researcher to collect data that either supports the hypothesis or rejects it.

Qualities of an Effective Hypothesis

An effective hypothesis has the following qualities:

  • It states as clearly and concisely as possible the expected relationship (or difference) between two or more variables.
  • It defines the selected variables in operational and measurable terms.
  • It is testable and verifiable. It is possible to support or not support the hypothesis by collecting and analyzing data.
  • The wordings are clear and precise.
  • It gives logical arguments to justify the hypothesis.
  • It is consistent with the existing body of knowledge.
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