Research Hypothesis

Research Hypothesis

The research or scientific hypothesis is a formal affirmative statement predicting a single research outcome, a tentative explanation of the relationship between two or more variables. For the hypothesis to be testable, the variables must be operationally defined. That is, the researcher specifies what operations were conducted, or tests used, to measure each variable. This, the hypothesis focuses the investigation on a definite target and determines what observations or measures, are to be used.

Several years ago the hypothesis was formulated that there is a positive causal relationship between cigarette smoking and the incidence of coronary heart disease (Best & Kahn, 2002: 11). This hypothesis proposed a tentative explanation that led to many studies comparing the incidence of heart disease among cigarette smokers and nonsmokers. As a result of these extensive studies, the medical profession now generally accepts that this relationship has been established.

In the behavioral sciences, the variables may be abstractions that cannot be observed. These variables must be defined operationally by describing some samples of actual behavior that are concrete enough to be observed directly. The relationship between these observable incidents may be deduced as consistent or inconsistent with the consequences of the hypothesis. Thus, the hypothesis may be judged to be probably true or probably false.

A hypothesis is a conjectural statement of the relation between neither two nor more variables. Hypotheses are always in declarative sentence form, and they relate, either generally or specifically, variables to variables. There are two criteria for "good" hypotheses and hypothesis statements. They are the same as two of those for problems and problem statements. One, hypotheses are statements about the relations between variables. Two, hypotheses carry clear implications for testing the stated relations. These criteria mean, then, that hypothesis statements contain two or more variables that are measurable or potentially measurable and that they specify how the variables are related. A statement that lacks either or both these characteristics are no hypothesis in the scientific sense of the word.

Theoretical Framework of Hypothesis

There is little doubt that hypotheses are important and indispensable tools of scientific research. There are three main reasons for this belief. One, they are, so to speak, the working instruments of theory. The hypothesis can be deduced from theory and from other hypotheses. If, for instance, we are working on a theory of aggression, we are presumably looking for causes and effects of aggressive behavior. We might have observed cases of aggressive behavior occurring after frustrating circumstances. The theory, then, might include the proposition; Frustration produces aggression. From this broad hypothesis we may deduce more specific hypotheses, such as: To prevent children from reaching goals they find desirable (frustration) will result in their fighting with each other (aggression); if children are deprived of parental love (frustration), they will react in part with aggressive behavior.

The second reason is that hypotheses can be tested and shown to be probably true or probably false. Isolated facts are not tested, as we said before; only relations are tested. Since hypotheses are relational propositions, this is probably the main reason they are used in scientific inquiry. They are, in essence, predictions of the form, "If A, then B," which we set up to test the relation between A and B. We let the facts have a chance to establish the probable truth or falsity of the hypothesis.

Three, hypotheses are powerful tools for the advancement of knowledge because they enable man to get outside himself. Though constructed by man, hypotheses exist, can be tested, and can be shown to be probably correct or incorrect apart from man's values and opinions. This is so important that we venture to say that there would be no science in any complete sense without hypotheses.

Just as important as hypotheses are the problems behind the hypotheses. As Dewey has well pointed out, research usually starts with a problem, with a problematic situation. Dewey says that there is a first and indeterminate situation in which ideas are vague, doubts are raised, and the thinker is perplexed. He further points out that the problem is not enunciated indeed cannot be enunciated until one has experienced such an indeterminate situation.

The indeterminacy, however, must ultimately be removed. Though it is true, as stated earlier, that a researcher may often have only a general and diffuse motion of his problem, sooner or later he has to have a fairly clear idea of what the problem is. Though this statement seems self-evident, one of the most difficult things to do, apparently, is to state one's research problem clearly and completely.

The theoretical framework is the foundation on which the entire thesis is based. It is a logically developed, described, and elaborated network of associations among variables that have been identified through such processes as interviews, observations, and literature survey. These variables are deemed relevant to the problem situation.

The preliminary survey of literature and information provides a solid foundation for developing a theoretical framework. The theoretical framework describes the relationships among the variables, elaborates the theory underlying these relations, and describes the nature and direction of the relationship. A good theoretical framework provides the logical base for developing testable hypotheses. Sekaran identifies five basic components that should be incorporated in any theoretical framework:

1. The variables considered relevant to the study should be clearly identified.
2. The discussions should state how two or more variables are related to each other.
3. If the nature and direction of the relationship can be theorized based on the findings from previous research, then there should be an indication in the discussions as to whether the relationships would be positive or negative.
4. There should be a clear explanation of why we would expect these relationships to exist. The arguments could be drawn from the previous research findings.
5. A schematic diagram of the theoretical framework should be given so that the reader can visualize the theorized relationships.

There are two approaches to hypothesis testing. The more established is the classical or sampling-theory approach; the second is known as the Bayesian approach. Classical statistics are found in all of the major statistics books and are widely used in research applications. This approach represents an objective view of probability in which the decision-making rests totally on an analysis of available sampling data. A hypothesis is established; it is rejected or fails to be rejected, based on the sample data collected.

Bayesian statistics are an extension of the classical approach. They also use sampling data for making decisions, but they go beyond them to consider all other available information. This additional information consists of subjective probability estimates stated in terms of degrees of belief. These subjective estimates are based on general experience rather than on specifically collected data. They are expressed as a prior distribution that can be revised after sample information is gathered. The revised estimate, and so on. Various decision rules are established, cost and other estimate scans are introduced, and the expected outcomes of combinations of these elements are used to judge decision alternatives. The Bayesian approach, based on the centuries-old Bayes theorem, has emerged as an alternative hypothesis-testing procedure since the mid-1950s.

Process of Hypothesis Formulation

The formulation of a hypothesis is a central step in good research. It is, therefore, important to give it a great deal of thought. All research problems may ultimately be reduced to the question: "which of a set of alternative means is the most efficient? Once we have the alternative means, many questions can be raised (for each means) about what would constitute evidence that these particular means is the most efficient one among the alternatives. The solution to this question usually is that" Particular means can be accepted as the most efficient among the alternatives under specific conditions.

Hypotheses are not given to us ready-made. This is so especially in social sciences where there has not yet evolved a highly developed theoretical system in many areas of its subject matter affording fruitful bases for hypothesis formulation. As a result, in social sciences at least, a considerable portion of research endeavor is directed understandably toward making hypotheses rather than testing them. Hence, it is to be remembered that research can begin with a well-formulated hypothesis (in the planning stage of the project) or it can come out with a hypothesis is as its end product (at a later stage).

In a classical test of significance, two kinds of hypotheses are used. The null hypothesis is used for testing. It is a statement that no difference exists between the parameter and the statistic being compared to it. A second, alternative hypothesis holds that there has been a change in average days outstanding. The alternative hypothesis is the logical opposite of the null hypothesis.

Frequently, statisticians formulate their hypotheses the exact opposite of what they may want to show. For instance, if we want to show that the students in one school have a higher average IQ than those in another school, we might formulate the hypothesis that there is no difference; the hypothesis u1 = u2. With this hypothesis we know what to expect, but this would not be the case if we formulated the hypothesis u1 > u2, at least not unless we specify the actual difference between u1 and u2.

Formulating a hypothesis for statistical significance follows a relatively well-defined pattern, although authors differ in the number and sequence of steps. One six-stage sequence is as follows:

1. State the null hypothesis
While the researcher is usually interested in testing a hypothesis of change or differences, the null hypothesis (Ho) is always used for statistical testing purposes.

2. Choose the statistical test
To test a hypothesis, one must choose an appropriate statistical test. There are many tests from which to choose, and there are at least four criteria that can be used in choosing a test. One is the power efficiency of the test. A more powerful test provides the same level of significance with a smaller sample than a less powerful test. In addition, in choosing a test, one can consider how the sample is drawn, the nature of the population, and the type of measurement scale used. For instance, some tests are useful only when the sequence of scores is known or when observations are paired. Other tests are appropriate only if the population has certain characteristics; still, other tests are useful only if the measurement scale is interval or ratio.

3. Select the desired level of significance
The choice of the level of significance should be made before we collect the data. The most common level is .05, although .01 is also widely used. Other levels such as .10,. 025, or .001 are sometimes chosen. The exact level to choose is largely determined by how much a risk one is willing to accept and the effect that this choice has on? risk. The larger the a the lower is the B.

4. Compute the calculated difference value
After the data are collected, use the formula for the appropriate significance test to obtain the calculated value.

5. Obtain the critical test value
After we compute the calculated t, F, Z, or another measure, we must look up the critical value in the appropriate table for that distribution. The critical value is the criterion that defines the region of rejection from the region of acceptance of the null hypothesis.

6. Interpret the test
For most tests, if the calculated value is larger than the critical value, we reject the null hypothesis and conclude that the alternative hypothesis is supported (although it is by no means proved). If the critical value is larger, we conclude we have a failure to reject the null.