Research hypotheses are somewhat different from research questions, where a hypothesis refers to a clearly articulated statement about the expected relationship between a set of variables and is found only in quantitative studies. Yet, a research question of a quantitative study can be turned into a hypothesis. Let’s take an example, quantitative research has a research question, “Do standardized college admissions tests predict academic success in college?”; thus, the hypothesis may be “high school GPA is a stronger predictor of academic success in college than standardized college admissions test.”
Hypotheses are formulated based on previous theories and findings on the topic being studied, and that they are testable. There are four components to hypotheses: a) variables, b) prediction to a relationship between variables, c) previous theories and logically derived from previous knowledge in the field, and d) test the relationship between variables. Firstly, a hypothesis must include variables–things that can change and take on different values–or a unit of measurement such as people, events, times, achievements, and so on. Secondly, a hypothesis predicts a relationship between particular variables; thus, it should clearly indicate the possible or tentative relationship whether it is non-directional, directional, causal, or correlational. Thirdly, a hypothesis is a tentative, carefully thought out logical statement of a predicted outcome which is supported by a rationale and must be consistent with existing theory. Finally, in predicting the relationship between variables, a hypothesis can be empirically tested.
There are various types of hypotheses for a study. First, we can formulate a simple hypothesis which predicts the relationship between a single dependent variable and a single independent variable. For example: students who attend more lectures get better grades. The second type is a complex hypothesis which suggests the relationship between more than two variables; maybe two independent variables and one dependent variable or vice versa. For example: people who both (1) eat a lot of fatty foods and (2) have a family history of health problems are more likely to develop heart diseases. Another type of hypothesis is a non-directional hypothesis where it does not predict the exact direction or nature of the relationship between the two variables, and it is used when there is no theory involved or when findings contradict previous research. For example: the bunching of buses is linked to traffic congestion. Then, there is a directional hypothesis that specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. For example: the extent of bunching of buses increases when the level of traffic congestion increases. Then, a causal hypothesis proposes an effect on the dependent variable due to the manipulation of the independent variable. For instance: the bunching of buses is caused by traffic congestion. Correlational hypothesis is also called associative hypothesis where it defines interdependency between variables where a change in one variable results in the change of the other variable. For instance: the extent of bunching of buses varies in accord with the level of traffic congestion. After formulating the hypothesis, we need to also provide the null hypothesis or a negative statement to support the researcher’s findings that there is no relationship between the variables under study. Meanwhile, the alternative hypothesis states that there is a relationship between the variables of the study and that the results are significant to the research topic.
When we test the hypothesis using statistical calculations, we can either accept or reject the alternative hypothesis. When the result is significant and fulfills the criteria of accepting the hypothesis, it means that the alternative hypothesis is accepted (there is a significant relationship between the variables. On the other hand, when the statistical result shows no significant relationship between the variables, it means that the null hypothesis is accepted and the alternative hypothesis is rejected.